Cited by 654Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot LearningRetinal Imaging and Analysis

The DOI matches its Crossref record; 77 of the 94 references it cites were found in a registry or an index; none of the 43 sources its claims cite is checked for support yet.

Quicksilver: Fast predictive image registration – A deep learning approach

Open access

Yang, Xiao, Kwitt, Roland, Styner, Martin, Niethammer, Marc·2017·NeuroImage

DOI 10.1016/j.neuroimage.2017.07.008PMC PMC6036629

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Abstract

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.

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.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.neuroimage.2017.07.008

Cites

77 of 77 references verified0 citation errors · 0 not checkable

Statistics

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Claims the paper makes

0 of 43 cited sources checked · 0 supported · 0 flagged

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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
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
Load-bearingStrength:boosted· not yet calibratedCausal:direct· not yet calibrated
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
“These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization”
§ Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach > Abstract
Load-bearingStrength:boosted· not yet calibratedCausal:correlational· not yet calibrated
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 calibrated
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.
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 calibrated
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.
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
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.

Show all 84 claims

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
Load-bearingStrength:boosted· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It 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.
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 calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It 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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably 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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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
“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 calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclear
It 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
Load-bearingStrength:boosted· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
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 calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It 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 calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably 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 calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably 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
“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 calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably 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 calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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 calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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
“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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.

63 of 63

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.”

2 of 2

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?

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
Minor
The four listed dataset counts sum to 2,088 registrations, not 2,168.
Reported 2168 · recomputed 2088
Multimodal test-pair count is inconsistent with 16 images
Minor
Pairwise directed registrations among 16 images yield 16 × 15 = 240 distinct-image cases, not 250.
Reported 250 · recomputed 240
Multimodal training-set size changes from 359 to 365
Minor
The dataset split explicitly assigns 359 images to training, but the later description calls the training set 365 images.
Reported prediction runtime exceeds its stated components
Minor
The 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

4 of 4

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×
“The network is implemented in PyTorch, and optimized using Adam [36], where we set the learning rate to 0.”
Method
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · cited 1×
“layer [24] to perform “unpooling” (Dec1(F ) in Fig.”
Background
Untitled work
(2014) · Cited by 33,419
Numerical optimization
Braun, W. J., & Murdoch, D. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108993456.011 · Cited by 14,099
Untitled work
(2010) · International Conference on Machine Learning · Cited by 13,995
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
Determining optical flow
Horn, B. K. P., & Schunck, B. G. (1981) · Artificial Intelligence · doi:10.1016/0004-3702(81)90024-2 · Cited by 10,312
Numerical Optimization
Nocedal, J. M., & Wright, S. J. (2006) · Springer series in operations research/Springer series in operations research and financial engineering · doi:10.1007/978-0-387-40065-5 · Cited by 10,206
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].”
Method
Determining Optical Flow
Horn, 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

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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
ORCID: Yang, Xiao
Kwitt, Roland
Styner, Martin
Niethammer, Marc
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2017-09
Volume: 158
Pages: 378-396
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2017.07.008
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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