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Quicksilver: Fast predictive image registration – A deep learning approach
Open accessAbstract
This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Quicksilver is a deep-learning registration method that predicts the initial momentum of LDDMM shooting patch-by-patch from moving/target image patches, with optional Bayesian uncertainty and a correction network. It achieves registration accuracy close to numerical LDDMM optimization on several brain MRI datasets while running much faster.
Key findings
- Predicting the initial momentum gave better atlas-to-image accuracy than predicting initial velocity or displacement; momentum and velocity networks produced positive Jacobian determinants in all tested cases, whereas direct displacement prediction did not guarantee diffeomorphic maps.
- On the four Klein et al. validation datasets, the prediction+correction model (LPC) approached numerical LDDMM optimization (LO): paired TOST indicated LPC was statistically equivalent to LO within a 2% relative threshold on all four datasets, and label overlap was top-tier with few outliers.
- Runtime for a 229×193×193 volume: LDDMM optimization took about 10.8 minutes, while Quicksilver prediction took 7.63 s plus 8.9 s for shooting, totaling 18.43 s, a roughly 35× speedup; adding the correction network doubled time but remained an order of magnitude faster than optimization. Patch pruning and stride 14 reduced the number of predicted patches by 99.995% for such volumes.
- Multimodal T1w–T2w prediction reduced deformation error relative to affine registration and was only slightly worse than the T1w–T1w upper bound; training with only 10 images (90 registration pairs) still worked with a small performance drop. Bayesian sampling produced uncertainty maps with high values near ventricles and large deformations.
Methods
- The method uses the LDDMM shooting formulation and trains a deep encoder-decoder network to predict vector-valued initial momentum from paired 15×15×15 patches of moving and target images. Whole-image prediction uses a sliding window with stride 14 and patch pruning, followed by LDDMM shooting to generate deformations.
- Data: atlas-to-image used 150 OASIS images (100 training, 50 testing); image-to-image used 373 OASIS images for training and tested on LPBA40, IBSR18, MGH10, and CUMC12, totaling 2,168 test registrations; multimodal used the IBIS 3D dataset with 375 T1w/T2w images (359 training, 16 testing, 250 test cases). A correction network was trained sequentially on the residual between ground-truth and predicted momentum; a probabilistic version used dropout (probability 0.2) and Monte Carlo sampling.
- Evaluation included voxel-wise deformation error percentiles, Jacobian determinant positivity, LDDMM energy, target overlap of labels, paired t-tests, paired TOST equivalence tests, and runtime/multi-GPU scaling.
Limitations
- The paper states that the 15×15×15 patch receptive field limits prediction of very large deformations; OASIS training data had only 0.2% of voxels with deformations above 10 mm, so large deformations were underrepresented. Outliers with inconsistent anatomy can cause extreme optimization deformations that prediction may not match, though the conservative prediction can also avoid implausible warps.
- The correction network doubles runtime, and applying correction multiple times can produce more drastic deformations and numerical instability; 12 LPP cases in LPBA40 failed LDDMM shooting due to integration problems. Patch pruning may not transfer to parametric models if parameter locality is not guaranteed, and multi-GPU scaling is limited by communication overhead and PyTorch’s beta-stage performance.
- Evident from the text: validation is restricted to brain MRI with a specific LDDMM regularizer and SSD similarity, so generalization to other organs, modalities, or registration models is not demonstrated.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our experiments show that our approach also predicts accurate deformations for multi-modal registration.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
In the paper
“achieves state-of-the-art registration results on four standard validation datasets”
- Also stated in: § 1. Introduction · boosted
“demonstrate registration accuracies on these datasets on par with the state-of-the-art”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > Abstract
What the sources say
- Not checked yetcites [33] Kingma, 2014
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
- Also stated in: § 1. Introduction · boosted
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Experiments show that the correction network improves registration results to the point where optimization-based and predicted registrations achieve a similar level of registration accuracy on registration validation experiments.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“is very fast”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“and can jointly learn an image similarity measure”
- Also stated in: § 1. Introduction · plain
“our framework can simultaneously predict registrations and learn a multi-modal image similarity measure”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
- Also stated in: § 1. Introduction · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For a typical 3D image of size 128 × 128 × 128 using a 15 × 15 × 15 patch for prediction will require more than 1.4 million patch predictions.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
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In the paper
“We observe that the initial momentum and the initial velocity networks indeed generate diffeomorphic deformations in all scenarios.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Predicting the displacement directly cannot guarantee diffeomorphic deformations even for a small stride.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In our experiments, such a combined network easily got stuck in poor local minima.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In contrast, the velocity and the displacement parameterizations result in slightly better predictions for smaller sliding window strides.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we observed that an independent decoder structure is much easier to train than a network with one large decoder (3 times the number of features of a single decoder in our network) to predict the initial momentum in all dimensions simultaneously.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“By using these two techniques, we can reduce the number of predicted patches for one single image dramatically. For example, by 99.995% for 3D brain images of dimension 229 × 193 × 193.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Experiments show that our prediction+correction approach results in lower training and testing error compared with only using a prediction network, as shown in Sec. 2.4 and Sec. 3.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The first observation is that patch-based prediction is accurate when the deformation inside the patch is small.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the deformation accuracy is significantly worse for the initial velocity network.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We also observe that the momentum prediction result using a smaller sliding window stride is slightly worse than the one using a stride of 14.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For both, using a correction network in conjunction with a prediction network results in lower training error compared with training the prediction network for more epochs.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.4. Datasets/Setup
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In the paper
“experiments do not show an obvious difference in prediction accuracy between using two independent encoders and one single large encoder.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
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In the paper
“We observe that the initial momentum network has better prediction accuracy compared to the results obtained via the initial velocity and displacement parameterization in both the 5-stride and 14-stride cases.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, Table 3 shows that our prediction+correction approach (LPC) performs similar (with respect to the other registration methods) to LO.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
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In the paper
“Performing LDDMM optimization using the GPU-based implementation of PyCA for a 229 × 193 × 193 3D brain image takes approximately 10.8 minutes.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Lastly, in contrast to many of the other methods Quicksilver produces virtually no outliers.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this example, the brain extraction did not extract consistent anatomy for the moving image and the target image. Specifically, only inconsistent parts of the cerebellum remain between the moving and the target images.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“LP generates smoother deformations compared with LO”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared to the LDDMM optimization approach, our method achieves a 35× speed up.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, also using the correction network boosts the deformation accuracy nearly to the same level as the LDDMM optimization approach (LO).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
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In the paper
“Similar arguments hold for LPC2 and LPC3: using the correction network multiple times (iteratively) in the correction step also results in increasingly drastic deformations, as illustrated by the curves for LPC, LPC2 and LPC3 in Fig. 7.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Also, when we increase the number of GPUs to 8, the prediction time slightly increases to 2.48s.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, there are 12 cases for LPP where the predicted momentum cannot generate deformation fields via LDDMM shooting using PyCA, due to problems related to numerical integration.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We can see that LPC is closest to LO.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, performance decreases are generally very small: −0.017, −0.013, and −0.009 mean decrease in target overlap ratio for the three aforementioned exceptions, respectively.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
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In the paper
“Again, using a correction network improves the prediction accuracy in all cases.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.3. Multi-modal image registration
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, as we will show in Sec. 3.2.3, LPP, LPC2 and LPC3 deviate from our goal of predicting deformations that are similar to the LDDMM optimization result (LO).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All three networks (T1w-T1w, T1w-T2w, T1w-T2w using 10 training images) generate warped images that are similar to the LDDMM optimization result.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.3. Multi-modal image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The uncertainty map shows high uncertainty along the ventricle areas where drastic deformations occur, as shown in the moving and target images.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: The local variance of deformation fields generated from multiple dropout samples provides an uncertainty estimate of the predicted deformation field.
In the paper
“We observe that direct numerical optimization of the shooting LDDMM formulation via PyCA (LO) is a highly competitive registration method and shows better target overlap scores than most of the other registration algorithms for all four datasets (LPBA40, IBSR18, CUMC12, and MGH10). Notable exceptions are ART (on LPBA40), SyN (on LBPA40), and SPM5D (on IBSR18).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
What the sources say
- Not checked yetcites [33] Kingma, 2014
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These problems can be observed in the LPBA40 results shown in Fig. 6, which show more outliers with low overlapping scores for LPP and LPC3.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As we can see, by increasing the number of GPUs, the initial momentum prediction time decreases from 11.23 seconds (using 1 GPU) to 2.41 seconds using 7 GPUs.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Impressively, by using multiple GPUs, the runtime can be improved by two orders of magnitude over a direct (GPU-based) LDDMM optimization.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Sec. 3.2.2, LPP, LPC2 and LPC3 all result in label overlapping scores which are similar to the label overlapping scores obtained via LPC.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
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In the paper
“The predicted deformation result is very similar to the deformation from LDDMM optimization.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“first, the LDDMM optimization performance is on par with SyN [68], ART [69] and the SPM5 DARTEL Toolbox (SPM5D) [70].”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
What the sources say
- Not checked yetcites [68] Yang, 2016
- Not checked yetcites [69] Yang, 2017
- Not checked yetcites [70] Zach, 2007, A duality based approach for realtime TV-L1 optical flow, Pattern Recognit.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, in the IBSR18 dataset, only three distinct images are needed as moving or target images to cover the 49 registration cases that generate deformations larger than 20 mm.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Strength:plain· not yet calibratedn = 49Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For very large deformations that occur for 1% of the total voxels, LPC does not drastically reduce the deformation error.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An interesting discovery is that LPP, LPC2 and LPC3 produce label overlapping scores that are on-par with LPC.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, for the networks trained using only 10 images, the performance only decreases slightly in comparison with the T1w-T2w multi-modal networks trained with 359 images.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.3. Multi-modal image registration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“It results in highly desirable diffeomorphic spatial transformations (if regularized sufficiently).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [17] Gal, 2016
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesWhat the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, our results are based on more than 2,400 image registration pairs.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We extend our network to a Bayesian model which is able to determine the uncertainty of the registration parameters and, as a result, the uncertainty of the deformation field.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
In the paper
“It can capture large deformations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [17] Gal, 2016
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesWhat the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Of note, these registration results are achieved using a model that was trained on an entirely different dataset (images from the OASIS dataset).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the momentum has compact support around image edges, no ambiguities arise within uniform image areas (in which predicting a velocity or deformation field would be difficult).”
- Also stated in: § 2.1. LDDMM Shooting · plain
“This is not a problem for the initial momentum parameterization, since the initial momentum in these areas, for image-based LDDMM, is zero.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 2.1. LDDMM Shooting · plain
In the paper
“However, optimizing over the vectorvalued momentum, m, instead is numerically better behaved [50], which is why we focus on it for our predictions.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.1. LDDMM Shooting
What the sources say
- Not checked yetcites [50] Shams, 2010, A survey of medical image registration on multicore and the GPU, IEEE Signal Process. Mag. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Chou et al. [10] propose a multi-scale linear regressor which only applies to affine deformations and low-rank approximations of non-linear deformations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [10] Cao, 2015
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, computation time is proportional to the number of the patches we need to predict.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“This is unsurprising as, similar to existing optical flow approaches [14, 15], directly predicting displacements does not encode deformation smoothness.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
What the sources say
- Not checked yetcites [14] Dosovitskiy, 2015
- Not checked yetcites [15] Dupuis, 1998, Variational problems on flows of diffeomorphisms for image matching, Q. Appl. Math. source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Instead, predicting the momentum itself patch-wise significantly simplifies the network training procedure.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Since K governs the theoretical properties or LDDMM, a strong K assures diffeomorphic transformations5”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.1. LDDMM Shooting
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is justified, because according to LDDMM theory the initial momentum in constant image regions, and hence also in the image background, should be zero.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This validates our hypothesis that momentum-based LDDMM is better suited for patch-wise deformation prediction.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This summation is justified from the LDDMM theory as it is performed in a fixed coordinate system (a fixed tangent space), which is the coordinate system of the moving image.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The complete iterative numerical approach typically used for LDDMM registration is replaced by a single prediction step. Hence, it is not possible to recover from any prediction errors.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Zhang et al. [7] propose a finite-dimensional approximation of LDDMM, achieving a roughly 25× speed-up over a standard LDDMM optimization-based solution.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [7] Borzi, 2003, Optimal control formulation for determining optical flow, SIAM J. Sci. Comput. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, a GPU-based registration of a 128 × 128 × 128 image volume using LDDMM will take about 10 minutes on a current GPU (e.g., a Nvidia TitanX).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“such a two-encoder strategy is beneficial when extending the approach to multi-modal image registration [36].”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
What the sources say
- Not checked yetcites [36] Lee, 2009
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Cao et al. [12] use a semi-coupled dictionary learning method to directly model the relationship between the image appearance and the deformation parameters of the LDDMM model [13]. However, only a linear relationship is assumed between image appearance and the deformation parameters.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [12] Chou, 2013, 2D/3D image registration using regression learning, CVIU
- Not checked yetcites [13] Chung, 2013, CLARITY for mapping the nervous system, Nat. Methods source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, it is hard to predict initial velocity or displacement for homogeneous image regions, as these regions locally provide no information from which to predict the spatial transformation.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.1. LDDMM Shooting
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“large deformations may result in drastic appearance changes between a source and a target patch. In the extreme case, corresponding image information may no longer be found for a given source and target patch pair.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, predicting alternative parameterizations such as the initial velocity or directly the displacement field would make it difficult to obtain diffeomorphic transformations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.1. LDDMM Shooting
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, this is not feasible in our formulation because of the limited memory in modern GPUs.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.1. LDDMM Shooting
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Runtimes in the tens of minutes are the norm for popular deformable image registration solutions.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When we use the prediction network in the correction step, the high label overlapping scores are due to more drastic deformations compared with LP”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As optimization-based LDDMM does not know about this inconsistency, it attempts to match the images as well as possible and thereby creates a very extreme deformation.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, numerical instability is likely due to the use of a fixed step size for this time-integration which is small enough for the deformations expected to occur for these brain registration tasks, but which may be too large for the more extreme momenta LPP and LPC3 create for some of these cases.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This demonstrates the capability of our network architecture to implicitly learn the complex similarity measure between two modalities.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.3. Multi-modal image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This demonstrates that the correction network is capable of improving the initial momentum prediction beyond the capabilities of the original prediction network.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such a small number of training patches containing very large deformations makes it difficult to train the network to accurately predict these very large deformations in the test data.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, our method can readily be used in a GPU-cluster environment for ultra-fast deformation prediction.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Third, outliers in the dataset whose appearances are very different from the other images in the dataset can cause very large deformations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, the input patch size of the deep learning framework is 15 × 15 × 15, which means that the receptive field for the network input is limited to 15 × 15 × 15mm3. This constrains the network’s ability to predict very large deformations”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, these statistical tests confirm that our prediction models, in particular LPC, are highly competitive-registration algorithms.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, even when using very limited image data, we can still successfully train our prediction networks when a sufficient number of patches is available.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.3. Multi-modal image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 46
- Probably not a substantial claim. 22
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We experimentally show that by using patch pruning and a large sliding window stride, our method achieves dramatic speedups compared to the optimization approach, while maintaining good registration accuracy.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the total number of 15 × 15 × 15 3D training patches to train the prediction network is 1,002,404. This amounts to approximately 3.4 billion voxels and is much larger than the total number of parameters in the network.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The number of 3D filters used in the network is 975,360. The overall number of parameters 21,826,344.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Compared to the label overlapping accuracy boost from LP to LPC, LPC2 and LPC3 do not greatly improve the registration accuracy, and may even generate worse results (e.g., LPC3 on LPBA40).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared with the initial LDDMM energy based on affine registration to the atlas space in the initial column, both LP and LPC have drastically lower LDDMM energy values; further, these values are only slightly higher than those for LO.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“LPP, LPC2 and LPC3 generate more drastic deformations (i.e., more spread out histograms indicating areas of stronger expansions and contractions).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, they produce more drastic deformations that can lead to worse label overlap and even numerical stability problems.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our prediction result, however, still generates reasonable deformations (where plausibility is based on the deformations that were observed during training) while matching the brain structures as much as possible.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using the correction network together with the prediction network doubles the computation time, but the overall runtime is still an order of magnitude faster than direct LDDMM optimization.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that, at a stride of 1, computational cost increases about 3000-fold in 3D, resulting in runtimes of about 5½ hours for 3D image registration (eleven hours when the correction network is also used).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using our prediction network with a sliding window stride of 14, the initial momentum prediction time is, on average, 7.63 seconds. Subsequent geodesic shooting to generate the deformation field takes 8.9 seconds, resulting in a total runtime of 18.43 seconds.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“For a relative threshold difference of 2% LPC can be considered statistically equivalent to LO for all four datasets and to many of the other top methods (e.g., LPC vs. SyN on MGH10 and IBSR18).”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
What the sources say
- Not checked yetcites [71] Zhang, 2015
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among the prediction models, LPC improves the prediction accuracy compared with LP, and generally achieves the highest deformation prediction accuracy for up to 80% of the voxels.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The multi-modal networks (T1w-T2w, LP/LPC) significantly reduce deformation error compared to affine registration, and only suffer a slight loss in accuracy compared to their T1w-T1w counterparts.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.3. Multi-modal image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, the deformations in the OASIS training images have a median of 2.609 mm, which is similar to the median observed in the four testing datasets. However, only 0.2% of the voxels in the OASIS training dataset have deformations larger than 10 mm.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A slight performance drop with respect to LO can be observed for LPC and a slightly bigger performance drop for LP, which only uses the prediction model, but no correction model.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, compared with LP, LPC generates LDDMM energy values that are closer to LO, which indicates that using the prediction+correction approach results in momenta which are closer to the optimal solution than the ones obtained by using the prediction network only.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is also on-par with other prediction models for up to 99% of the voxels, where the largest deformations are in the range between 7.317 mm-9.026 mm for the four datasets.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared with other methods, our prediction+correction network achieves top-tier performance for label matching accuracy at a small fraction of the computational cost.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, using only the prediction network results in a slight performance drop compared to the numerical optimization results (LO), but the result is still competitive with the top-performing registration methods.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in the table, the largest deformations that LDDMM optimization generates are 23.393 mm for LPBA40, 36.263 mm for IBSR18, 18.753 mm for CUMC12 and 18.727 mm for MGH10.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“It is based on Riemannian geometry, and hence induces a distance metric on the space of images.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [17] Gal, 2016
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“It uses the initial momentum as the registration parameter, which does not need to be spatially smooth, and hence can be predicted patch-by-patch, and from which the whole geodesic path can be computed.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [17] Gal, 2016
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the initial momentum is generally not smooth, but is compactly supported at image edges”
- Also stated in: § 1. Introduction · hedged
“the momentum is typically compactly supported around image edges”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.1. LDDMM Shooting
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
- Also stated in: § 1. Introduction · hedged
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the initial velocity is generated by applying a smoothing kernel K to the initial momentum. Therefore, the smoothness of the deformation does not need to be specifically considered during the parameter prediction step, but is imposed after the prediction.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.1. LDDMM Shooting
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, by predicting the momentum, we retain all the convenient mathematical properties of LDDMM and, at the same time, are able to predict diffeomorphic transformations fast.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“We demonstrate that the momentum-parameterization for LDDMM shooting [17] is a convenient representation for regression approaches as (i) the momentum is typically compactly supported around image edges and (ii) there are no smoothness requirements on the momentum itself.”
- Also stated in: § Abstract · plain
“Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [17] Gal, 2016
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § Abstract · plain
None of the sources cited for this claim have been checked yet.
In the paper
“General approaches address multimodal image registration by either performing image synthesis [22, 23] to change the problem to a uni-modal image registration task, or by proposing complex, hand-crafted [24, 25, 26, 27] or learned [28, 29, 30, 31, 32] multi-modal image similarity measures.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [22] Gutierrez-Becker, 2017, Guiding multimodal registration with learned optimization updates, MedIA
- Not checked yetcites [23] Gutiérrez-Becker, 2016
- Not checked yetcites [24] Haber, 2007, Image registration with guaranteed displacement regularity, Int. J. Comput. Vis. source
- Not checked yetcites [25] Hart, 2009, An optimal control approach for deformable registration
- Not checked yetcites [26] Hazlett, 2017, Early brain development in infants at high risk for autism spectrum disorder, Nature source
- Not checked yetcites [27] He, 2015
- Not checked yetcites [28] Hermosillo, 2002, Variational methods for multimodal image matching, IJCV source
- Not checked yetcites [29] Hill, 2001, Medical image registration, Phys. Med. Biol. source
- Not checked yetcites [30] Hong, 2012
- Not checked yetcites [31] Horn, 1981, Determining optical flow, Artif. Intell. source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because M and T ○ Φ are in the same coordinate system, the differences between these two images are small as long as the predicted deformation is reasonable, and more accurate predictions can be expected.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Gal et al. [58] show that ELBO maximization is achieved by training with dropout [57].”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
What the sources say
- Not checked yetcites [58] Vaillant, 2004, Statistics on diffeomorphisms via tangent space representations, NeuroImage source
- Not checked yetcites [57] Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, JMLR
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is reasonable for our initial momentum parameterization because of the compact support (at edges) of the initial momentum and the spatial shift invariance we obtain via the pooling/unpooling operations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“for image-based LDDMM [17, 19, 45] the momentum can be written as m(x,t) = λ(x,t)∇I(x,t), where λ is a scalar field and ∇I is the spatial gradient of the image. Hence, for homogeneous areas, ∇I = 0 and consequentially m = 0.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.1. LDDMM Shooting
What the sources say
- Not checked yetcites [17] Gal, 2016
- Not checked yetcites [19] Grabner, 2006, Symmetric Atlasing and Model Based Segmentation: an Application to the hippocampus in Older Adults
- Not checked yetcites [45] Niethammer, 2011
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Multi-modal image registration is, in general, significantly more difficult than uni-modal image registration since image appearance can change drastically between different modalities.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“These experiments indicated that increasing the training data size further only results in marginal improvements, which are clearly outperformed by a combined prediction + correction strategy.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.4. Datasets/Setup
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“recent research [56] suggests that the degrees of freedom for a deep network can be significantly smaller than the number of its parameters.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
What the sources say
- Not checked yetcites [56] Springenberg, 2014
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“using dropout [57], which can be viewed as (Bernoulli) approximate inference in Bayesian neural networks [58, 59].”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
What the sources say
- Not checked yetcites [57] Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, JMLR
- Not checked yetcites [58] Vaillant, 2004, Statistics on diffeomorphisms via tangent space representations, NeuroImage source
- Not checked yetcites [59] Van Essen, 2013, WU-Minn HCP Consortium, the WU-Minn human connectome project: an overview, NeuroImage source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We chose the 1-norm instead of the 2-norm as our loss function to be able to tolerate outliers and to generate sharper momentum predictions.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“given the initial momentum, we are able to generate the whole geodesic path using the geodesic shooting equations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Assuming no flips of the entire coordinate system, a diffeomorphic deformation map should have positive Jacobian determinants everywhere, otherwise foldings occur in the deformation maps.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Gutierrez et al. [9] use a regression forest and gradient boosted trees [8] based on hand-crafted features to learn update steps for a rigid and a B-spline registration model.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [9] Cao, 2014, Multi-modal registration for correlative microscopy using image analogies, MedIA
- Not checked yetcites [8] Bruveris, 2012, Mixture of kernels and iterated semidirect product of diffeomorphisms groups, Multiscale Model. Simul. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Wang et al. [11] predict deformations by key-point matching using sparse learning followed by dense deformation field generation with radial basis function interpolation. The performance of the method heavily depends on the accuracy of the key point selection.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 1. Introduction
What the sources say
- Not checked yetcites [11] Cheng, 2015, Deep similarity learning for multimodal medical images, Comput. Methods Biomech. Biomed. Eng. Imaging Vis.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the correction for the initial momentum is then performed in the original coordinate space (of the moving image) which allows us to obtain an overall corrected initial momentum, m0. This is for example a useful property when the goal is to do statistics with respect to a fixed coordinate system, for example, an atlas coordinate system.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While using larger patches would be an option, this would require a network with substantially larger capacity (to store the information for larger image patches and all meaningful deformations) and would also likely require much larger training datasets8.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“In contrast to ReLU, PReLU avoids a zero gradient for negative inputs, effectively improving the network performance.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
What the sources say
- Not checked yetcites [52] Simpson, 2011
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For testing, we keep the dropout layers enabled to maintain the probabilistic property of the network”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“That this is not the case for the momentum parameterization suggests that it is easier for the network to learn to predict the momentum, as it indeed has become more specialized to the training data which was obtained with a stride of 14.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To address these shortcomings, we propose a two-step prediction approach to improve overall prediction accuracy.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.3. Correction network
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“the vector-valued momentum has been found to numerically better behave for LDDMM optimizations [50].”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.2. Deep network for LDDMM prediction
What the sources say
- Not checked yetcites [50] Shams, 2010, A survey of medical image registration on multicore and the GPU, IEEE Signal Process. Mag. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One of the important properties of LDDMM shooting is its ability to generate diffeomorphic deformations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, the initial momentum parameterization is the preferred choice among our three tested parameterizations as it achieves the best prediction accuracy and guarantees diffeomorphic deformations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.1. Atlas-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is reasonable as these methods are all non-parametric diffeomorphic or homeomorphic registration methods, allowing the modeling of large deformations between image pairs.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The proposed correction network is a step towards highly accurate deformation prediction, while only decreasing the computation speed by a factor of 2.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 4. Discussion
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that among these methods, only LPC is specifically trained to match the LDDMM optimization results and in particular to predict corrections to the initial momentum obtained by the prediction model (LP) in the tangent space of the moving image. In contrast, LPP, LPC2 and LPC3 lack this theoretical motivation.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using a smaller step-size would regain numerical stability in this case.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, note that, in general, a low energy will only imply a good registration result if the registration model is fully appropriate for the registration task.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, as the number of GPUs increases, the communication overhead between GPUs becomes larger which explains why computation time does not equal to 11.23/number of GPUs seconds.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence the initial momentum parameterization, which can tolerate large sliding window strides, is essential for fast deformation prediction with high accuracy while guaranteeing diffeomorphic deformations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.4. Runtime study
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These large deformations created by numerical LDDMM optimization are not always desirable; and consequentially registration errors of the prediction models with respect to the numerical optimization result are in fact sometimes preferred.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, the patch-wise prediction approach of our methods enables a multi-GPU implementation, further increasing the prediction speed.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 4. Discussion
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This can be regarded as an advantage of our network, where the conservative nature of patch-wise momentum prediction is more likely to generate reasonable deformations.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 3. Results > 3.2. Image-to-Image registration
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The proposed approach allows taking large strides for patch-wise prediction, without a substantial decrease in registration accuracy, resulting in fast and accurate deformation prediction.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 4. Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our framework is very general and can be directly applied to many other registration techniques.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 4. Discussion
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our method retains all theoretical properties of LDDMM and results in diffeomorphic transformations if appropriately regularized, but computes these transformations an order of magnitude faster than a GPU-based optimization for the LDDMM model.”
- Also stated in: § 4. Discussion · plain
“In effect, our Quicksilver registration approach converts a notoriously slow and memory-hungry registration approach to a fast method, while retaining all of its appealing mathematical properties.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 4. Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 4. Discussion · plain
The authors state this as an assumption.
A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
“We assume that we already have a set of LDDMM parameters which result in good registration results.”
The paper relies on this without support.
The local variance of deformation fields generated from multiple dropout samples provides an uncertainty estimate of the predicted deformation field.
“The local variance of these deformation fields can then be used as an uncertainty estimate of the predicted deformation field.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Some of these approaches report interval estimates, either by reformulating the registration objective function as a probability distribution and drawing samples (Risholm et al., 2013; Le Folgoc et al., 2016; Schultz et al., 2018), approximating the sampling process using test-time NN drop-out (Yang et al., 2017) or sampling using the test-time deformation covariance matrices embedded within a”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Yang et al., 2017
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Yang et al. [323] implemented stacked convolutional layers as an encoder-decoder approach to predict the morphing of the input pixel into its last formation using MRI brain scans from the OASIS dataset. They employed a registration model known as Large Deformation Diffeomorphic Metric Mapping (LDDMM) and attained remarkable enhancements in computation time.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [323]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Image-to-image test total does not match the dataset counts
MinorThe four listed dataset counts sum to 2,088 registrations, not 2,168.
Reported 2168 · recomputed 2088
“resulting in a total of 2168 registration (1560 from LPBA40, 306 from IBSR18, 90 from MGH10, 132 from CUMC12) test cases.”
Multimodal test-pair count is inconsistent with 16 images
MinorPairwise directed registrations among 16 images yield 16 × 15 = 240 distinct-image cases, not 250.
Reported 250 · recomputed 240
“For testing, we perform pair-wise T1w-T2w registrations for all 16 test images, resulting in 250 test cases.”
Multimodal training-set size changes from 359 to 365
MinorThe dataset split explicitly assigns 359 images to training, but the later description calls the training set 365 images.
“using only 10 of the 365 training images which are randomly chosen for training.”
“We select 359 of the images for training and use the remaining 16 images for testing.”
Reported prediction runtime exceeds its stated components
MinorThe stated prediction and shooting times add to 16.53 seconds, rather than the reported total of 18.43 seconds.
Reported 18.43 · recomputed 16.53
“the initial momentum prediction time is, on average, 7.63 seconds. Subsequent geodesic shooting to generate the deformation field takes 8.9 seconds, resulting in a total runtime of 18.43 seconds.”
Citations
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 2×
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“layer [24] to perform “unpooling” (Dec1(F ) in Fig.”
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Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
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A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122 · cited 3×
“Several points should be noted: first, the LDDMM optimization performance is on par with SyN [68], ART [69] and the SPM5 DARTEL Toolbox ( SPM5D ) [70].”
MethodHorn, B. K. P., & Schunck, B. G. (1981) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.965761 · Cited by 7,443 · cited 1×
“Optical flow approaches, such as the classical Horn and Schunck optical flow [41], the more recent total variation approaches [42], or methods based on linear elasticity theory [1] are examples for displacementbased registration formulations.”
Method
Flagged first, then by citations
17 of the 94 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803
“Studies have shown that deep learning leads to generally more consistent registrations and is an order of magnitude faster than more conventional methods 79 .”
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J. V., & Dalca, A. V. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2897538 · Cited by 2,334
“Most of these rely on ground truth warp fields [35]–[39], which are either obtained by simulating deformations and deformed images, or running classical registration methods on pairs of scans.”
BackgroundAn overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
“For example: deformable image registration [97,239]; model-to-image registration [240,241]; MRI-based attenuation correction for PET [242,243]; PET/MRI dose calculation [244]; unsupervised end-to-end learning for deformable registration of 2D CT/MR images [245]; an unsupervised learning model for deformable, pairwise…”
BackgroundMethodDeep Learning Applications in Medical Image Analysis
Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,574
“[83] stacked convolution layers in an encoder-decoder fashion, to predict how an input pixel would morph into its final configuration.”
Background
Metadata
- Authors
- Yang, X., Kwitt, R., Styner, M., & Niethammer, M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of Salzburg (AT)
- Corresponding author
- Yang, Xiao
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 158
- Pages
- 378-396
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC6036629
- OpenAlex
- W2604920239
- Cited by
- 630
- References
- 94
- Citations by year
- 2026: 17 · 2025: 53 · 2024: 74 · 2023: 65 · 2022: 101 · 2021: 85 · 2020: 95 · 2019: 93 · 2018: 40 · 2017: 7
- FWCI
- 26.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 320 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- NeuroImage (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (1 R41 NS091792-01, HDO55741)
- NSF (ECCS-1148870, EECS-1711776)
- National Science Foundation Major Research Instrumentation program (ACI-1429830)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- deformable image registration, LDDMM, encoder-decoder architecture, multimodal image registration, image similarity measures, diffeomorphic registration
- MeSH terms
- Machine Learning, Algorithms, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Pattern Recognition, Automated, Imaging, Three-Dimensional
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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