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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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47 Own result · 41 Own interpretation · 28 No basis given · 26 Not checked yet · 5 Own method
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: A set of LDDMM parameters obtained by numerical optimization already exists and yields good registration results.
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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
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- 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
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- 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
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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
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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
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In the paper
“is very fast”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > Abstract
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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
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- Also stated in: § 1. Introduction · plain
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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
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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
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- It may state two separate claims as one. 46
- Probably not a substantial claim. 22
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“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
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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
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“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
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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
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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
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“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
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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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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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
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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
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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
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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
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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
boosteddirect- Asserted: passes — The authors put it forward as true.
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- 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
plain- Asserted: passes — The authors put it forward as true.
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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
plainconditional- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
boosteddirect- Asserted: passes — The authors put it forward as true.
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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
hedged- Asserted: passes — The authors put it forward as true.
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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
plain- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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.
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plain- Asserted: passes — The authors put it forward as true.
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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
hedgeddirectn = 12- Asserted: passes — The authors put it forward as true.
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Probably 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
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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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
plainconditionaln = 49- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
hedgedconditionalSingle case- Asserted: passes — The authors put it forward as true.
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It 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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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]
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Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“resulting in a total of 2168 registration (1560 from LPBA40, 306 from IBSR18, 90 from MGH10, 132 from CUMC12) test cases.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.4. Datasets/Setup
Reported 2168 · recomputed 2088
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“For testing, we perform pair-wise T1w-T2w registrations for all 16 test images, resulting in 250 test cases.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.4. Datasets/Setup
Reported 250 · recomputed 240
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.4. Datasets/Setup
“using only 10 of the 365 training images which are randomly chosen for training.”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > 2. Materials and Methods > 2.4. Datasets/Setup
“We select 359 of the images for training and use the remaining 16 images for testing.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“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
Reported 18.43 · recomputed 16.53
Citations
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Within-subject template estimation for unbiased longitudinal image analysis
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MethodEvaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
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BackgroundComputing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
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BackgroundMethodInfluentialA Duality Based Approach for Realtime TV-L 1 Optical Flow
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MethodDiffeomorphic demons: Efficient non-parametric image registration
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MethodDeformable medical image registration: a survey
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BackgroundEarly brain development in infants at high risk for autism spectrum disorder
The IBIS Network, Hazlett, H. C., Gu, H., Munsell, B. C., Kim, S. H., Styner, M., Wolff, J. J., Elison, J. T., Swanson, M. R., Zhu, H., et al. (2017) · Nature · doi:10.1038/nature21369 · Cited by 1,128
Self-citationDeepFlow: Large Displacement Optical Flow with Deep Matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., & Schmid, C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.175 · Cited by 1,057 · cited 3×
“This is unsurprising as, similar to existing optical flow approaches [12, 13], directly predicting”
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Numerical Methods for Image Registration
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“Image registration is typically formulated as an optimization problem [1], optimizing the parameters of a transformation model.”
MethodUnbiased diffeomorphic atlas construction for computational anatomy
Joshi, S. C., Davis, B. C., Jomier, M., & Gerig, G. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.068 · Cited by 881 · cited 1×
“We create an unbiased atlas [31] from all training data using PyCA(5) [32, 33], and use the atlas as the”
MethodFlowNet: Learning Optical Flow with Convolutional NetworksPreprint
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BackgroundMethodMedical image registration: a review
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“Due to the importance of image registration, a large number of different approaches have been proposed [1, 37, 38, 39].”
BackgroundBayesian Convolutional Neural Networks with Bernoulli Approximate Variational InferencePreprint
Gal, Y., & Ghahramani, Z. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.02158 · Cited by 777 · cited 2×
“We extend our architecture to a probabilistic network using dropout [27], which can be viewed as (Bernoulli) approximate inference in Bayesian neural networks [28, 29].”
BackgroundMethodCLARITY for mapping the nervous system
Chung, K., & Deisseroth, K. (2013) · Nature Methods · doi:10.1038/nmeth.2481 · Cited by 747 · cited 1×
“Even more dramatically, the microscopy field now routinely generates gigabytes of highresolution imaging data, for example, by 3D imaging via tissue clearing [4].”
BackgroundA theoretical framework for back-propagation
LeCun, Y. (1988) · Cited by 583
Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults
Grabner, G., Janke, A. L., Budge, M. M., Smith, D. B., Pruessner, J. C., & Collins, D. L. (2006) · Lecture notes in computer science · doi:10.1007/11866763_8 · Cited by 556 · cited 1×
“All images used in our experiments are first affinely registered to the ICBM MNI152 nonlinear atlas [65] This regularizer is too weak to assure a diffeomorphic transformation based on the sufficient regularity conditions discussed in [13].”
MethodA CNN Regression Approach for Real-Time 2D/3D Registration
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“Deep learning has been used for optical flow estimation [14, 15] and deformation parameter prediction for affine transformations [16].”
MethodDiffeomorphic registration using geodesic shooting and Gauss–Newton optimisation
Ashburner, J., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.049 · Cited by 494 · cited 1×
“For example, Ashburner and Friston [6] use a Gauss–Newton method to accelerate convergence for LDDMM and Zhang et al. [7] propose a finite-dimensional approximation of LDDMM, achieving a roughly 25 speed-up over a standard LDDMM optimization-based solution.”
MethodVariational problems on flows of diffeomorphisms for image matching
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“However, as long as the obtained velocity fields v are finite over the unit interval, i.e., 1 0 v 2 L dt ∞ for an L of at least order 6, we will obtain a diffeomorphic transform [51].”
BackgroundVariational Methods for Multimodal Image Matching
Hermosillo, G., Chefd'Hotel, C., & Faugeras, O. (2002) · International Journal of Computer Vision · doi:10.1023/a:1020830525823 · Cited by 388
Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention
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Testing Statistical Hypotheses of Equivalence
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“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].”
MethodA Deep Metric for Multimodal Registration
Simonovsky, M., Gutiérrez-Becker, B., Mateus, D., Navab, N., & Nikos Komodakis, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_2 · Cited by 224 · cited 1×
“General approaches address multi-modal image registration by either performing image synthesis [22, 23] to change the problem to an 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.”
BackgroundStatistics on diffeomorphisms via tangent space representations
Vaillant, M., Miller, M. I., Younes, L., & Trouvé, A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.023 · Cited by 215 · cited 1×
“The LDDMM shooting model in of itself is important for various image analysis tasks such as principal component analysis [16] and image regression [17, 18].”
MethodProceedings of the 1988 connectionist models summer school
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A Survey of Medical Image Registration on Multicore and the GPU
Shams, R., Sadeghi, P., Kennedy, R., & Hartley, R. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2009.935387 · Cited by 198 · cited 1×
“Attempts at speeding-up deformable image registration have primarily focused on GPU implementations [5], with impressive speed-ups over their CPU-based counterparts.”
BackgroundDiffeomorphic 3D Image Registration via Geodesic Shooting Using an Efficient Adjoint Calculation
Vialard, F., Risser, L., Rueckert, D., & Cotter, C. J. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0481-8 · Cited by 197 · cited 4×
“LDDMM has an alternative formulation, the shooting formulation [15], which parameterizes the deformation via the initial momentum vector field m0 = m(0), from which the map Φ can be computed.”
BackgroundMethodInfluentialDeep similarity learning for multimodal medical images
Cheng, X., Zhang, L., & Zheng, Y. (2016) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2015.1135299 · Cited by 158
Geodesic Regression for Image Time-Series
Niethammer, M., Huang, Y., & Vialard, F. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23629-7_80 · Cited by 139 · cited 2×
“This can be seen as for image-based LDDMM [15, 17] 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.”
BackgroundMethodSelf-citationFast Predictive Image Registration
Yang, X., Kwitt, R., & Niethammer, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46976-8_6 · Cited by 127 · cited 1×
“The registration method described here, which we name Quicksilver, is an extension of preliminary ideas we presented in a recent workshop paper [21].”
MethodSelf-citationMulti-modal image set registration and atlas formation
Lorenzen, P., Prastawa, M. W., Davis, B. C., Gerig, G., Bullitt, E., & Joshi, S. C. (2005) · Medical Image Analysis · doi:10.1016/j.media.2005.03.002 · Cited by 120 · cited 1×
“eral approaches address multimodal image registration by either performing image synthesis [22,23] to change the problem to an 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. In contrast, wedemonstratethat our fr…”
Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline
Wang, J., Vachet, C., Rumple, A., Gouttard, S., Ouziel, C., Perrot, E., Du, G., Huang, X., Gerig, G., & Styner, M. A. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00007 · Cited by 118 · cited 1×
“resulting in 90 registration pairs. We then use these 90 registration cases to train the multi-modal prediction networks. For skull stripping, we use FreeSurfer [63] for the OASIS dataset and AutoSeg [64] for the IBIS dataset. The 9https://bitbucket.org/scicompanat/pyca 10 Epoch L 1 loss per patch 1 1.5 2 2.5 3 3.5 Pr…”
MethodInfluentialSelf-citationImage Registration with Guaranteed Displacement Regularity
Haber, E., & Modersitzki, J. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-8984-4 · Cited by 88 · cited 1×
“example, by repeating registration and image warping steps) and the second, for example, by explicitly enforcing image regularity by constraining the determinant of the Jacobian of the transformation [43]. An alternative approach to allow for large deformations, while assuring dieomorphic transformations, is to parame…”
BackgroundOptimal Control Formulation for Determining Optical Flow
Borzì, A., Ito, K., & Kunisch, K. (2003) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827501386481 · Cited by 82 · cited 1×
“This adjoint system can easily be determined via a constrained optimization approach [45] (see [46] for the case of optical flow).”
BackgroundSingh, N., Hinkle, J. D., Joshi, S. C., & Fletcher, P. T. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556700 · Cited by 70 · cited 9×
“PyCA uses an explicit Runge-Kutta method (RK4) for time-integration.”
MethodInfluentialLearning Based Non-rigid Multi-modal Image Registration Using Kullback-Leibler Divergence
Guetter, C., Xu, C., Sauer, F., & Joachim Hornegger, A. (2005) · Lecture notes in computer science · doi:10.1007/11566489_32 · Cited by 69 · cited 1×
“General approaches address multi-modal image registration by either performing image synthesis [22, 23] to change the problem to an 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.”
BackgroundFinite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration
Zhang, M., & Fletcher, P. T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_19 · Cited by 63 · cited 1×
“[6] propose a finite-dimensional approximation of LDDMM achieving a roughly 25x speed-up over a standard LDDMM optimization-based solution.”
Background2D/3D image registration using regression learning
Chou, C.-R., Frederick, C. B., Mageras, G. S., Chang, S., & Pizer, S. M. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.02.009 · Cited by 62 · cited 2×
“An alternative approach to improve registration speed is to predict deformation parameters, or deformation parameter update steps in the optimization via a regression model, instead of directly minimizing a registration energy [7, 8].”
BackgroundMethodLearning similarity measure for multi-modal 3D image registration
Lee, D., Hofmann, M., Steinke, F., Altün, Y., Cahill, N. D., & Schölkopf, B. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206840 · Cited by 61
An optimal control approach for deformable registration
Hart, G. L., Zach, C., & Niethammer, M. (2009) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2009.5204344 · Cited by 57 · cited 4×
“This can be seen as 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.”
BackgroundMethodInfluentialSelf-citationMulti-modal registration for correlative microscopy using image analogies
Cao, T., Zach, C., Modla, S., Powell, D., Czymmek, K. J., & Niethammer, M. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.12.005 · Cited by 47 · cited 1×
“uni-modal image registration since image appearance can change drastically between different modalities. General approaches address multimodal image registration by either performing image synthesis [22,23] to change the problem to an uni-modal image registration task, or by proposing complex, hand-crafted [24,25,26,2…”
BackgroundSelf-citationLongitudinal Brain MRI Analysis with Uncertain Registration
Simpson, I., Woolrich, M. W., Groves, A. R., & Schnabel, J. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23629-7_79 · Cited by 40 · cited 1×
“, for uncertainty-based smoothing [19], or for surgical treatment planning, or could be directly visualized for qualitative analyses.”
BackgroundPredict brain MR image registration via sparse learning of appearance and transformation
Wang, Q., Kim, M., Shi, Y., Guorong Wu, A., & Shen, D. G. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.10.007 · Cited by 38 · cited 1×
“[9] predict deformations by key-point matching using sparse learning followed by dense deformation field generation with radial basis function interpolation.”
MethodA Hierarchical Geodesic Model for Diffeomorphic Longitudinal Shape Analysis
Singh, N. P., Hinkle, J. D., Joshi, S. C., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38868-2_47 · Cited by 35 · cited 1×
“We create an unbiased atlas [31] from all training data using PyCA(5) [32, 33], and use the atlas as the”
MethodBoosted metric learning for 3D multi-modal deformable registration
Michel, F., Bronstein, M. M., Bronstein, A. M., & Nikos Paragios, A. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872619 · Cited by 27 · cited 1×
“General approaches address multi-modal image registration by either performing image synthesis [22, 23] to change the problem to an 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.”
BackgroundA Deep Metric for Multimodal RegistrationPreprint
Simonovsky, M., Gutiérrez-Becker, B., Mateus, D., Navab, N., & Nikos Komodakis, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.05396 · Cited by 27 · cited 1×
“General approaches address multi-modal image registration by either performing image synthesis [22, 23] to change the problem to an 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.”
BackgroundFast predictive multimodal image registration
Yang, X., Kwitt, R., Styner, M. A., & Niethammer, M. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950652 · Cited by 27 · cited 3×
“4In fact, we have successfully trained prediction models with as little as ten images using all combinations of pair-wise registrations to create training data [26].”
BackgroundMethodInfluentialSelf-citationMixture of Kernels and Iterated Semidirect Product of Diffeomorphisms Groups
Bruveris, M., Risser, L., & Vialard, F.-X. (2012) · Multiscale Modeling & Simulation · doi:10.1137/110846324 · Cited by 25
Learning Optimization Updates for Multimodal Registration
Gutiérrez-Becker, B., Mateus, D., Peter, L., & Navab, N. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_3 · Cited by 24 · cited 2×
“[7] use a regression forest based on hand-crafted features to learn update steps for a B-spline registration model.”
MethodGuiding multimodal registration with learned optimization updates
Gutiérrez-Becker, B., Mateus, D., Peter, L., & Navab, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.05.002 · Cited by 19 · cited 2×
“[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.”
MethodEvaluation of control point selection in automatic, mutual information driven, 3D warping
Meyer, C., Boes, J. L., Kim, B., & Bland, P. H. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056283 · Cited by 17 · cited 1×
“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.”
BackgroundSemi-coupled dictionary learning for deformation prediction
Cao, T., Singh, N., Jojic, V., & Niethammer, M. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163967 · Cited by 13 · cited 1×
“[10] use a semi-coupled dictionary learning method to directly model the relationship between image appearance and the deformation parameters of the LDDMM model [11].”
MethodSelf-citationSimple Geodesic Regression for Image Time-Series
Hong, Y., Shi, Y., Styner, M., et al. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-31340-0_2 · Cited by 13 · cited 1×
“The LDDMM shooting model in of itself is important for various image analysis tasks such as principal component analysis [16] and image regression [17, 18].”
MethodSelf-citationFast Predictive Multimodal Image RegistrationPreprint
Yang, X., Kwitt, R., Styner, M. A., & Niethammer, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10902 · Cited by 5 · cited 3×
“4In fact, we have successfully trained prediction models with as little as ten images using all combinations of pair-wise registrations to create training data [26].”
BackgroundMethodInfluentialSelf-citation
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
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