Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation

Adrian V. Dalca; Hyeon Woo Lee; Mert R. Sabuncu

REVIEW 3 major objections 5 minor 39 references

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Brain MRI segmentation with only three labeled atlases lands within four overlap-score points of full supervision, and the proposed semi-supervised variant improves boundary accuracy.

desk verdict A solid empirical study with a plausible central claim; the quantitative 'within four Dice points' needs error bars, but the core finding deserves serious review. read the letter →

arxiv 1908.04466 v4 pith:3BZ65UZQ submitted 2019-08-13 cs.CV

Hyeon Woo Lee , Mert R. Sabuncu , Adrian V. Dalca This is my paper · ORCID

classification cs.CV

keywords brainMRIsegmentationfewlabeledatlasesmulti-atlassemi-supervisedregistrationdeeplearningdataaugmentationDicescoresurfacedistance

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks whether deep-learning-based brain MRI segmentation can work when only a handful of manually labeled atlases are available, a common clinical situation because annotations are expensive. It compares two paradigms: multi-atlas segmentation, where a learned registration network warps atlas labels onto a new scan, and supervised segmentation, where a convolutional network is trained directly on the labeled scans with random smooth deformations as augmentation. The central result is that both paradigms perform well: with just three labeled atlases, the best variant of each is within four overlap-score points of a model trained on all available labels. The paper also proposes a semi-supervised registration variant that occasionally trains the registration network on atlas-to-atlas pairs with a label-overlap loss, which improves Dice scores at every atlas count and substantially reduces boundary errors. If these findings hold, they contradict the usual assumption that deep-learning segmentation requires large labeled training sets.

What carries the argument

The load-bearing object is a learning-based registration function $g_{\theta}(I,I^*)=\phi$ implemented as a convolutional encoder-decoder network, which warps one image onto another. It is trained with an unsupervised loss $L_{\text{img}} + \lambda L_{\text{smooth}}$ on pairs of one atlas and one unlabeled scan; the proposed semi-supervised extension occasionally feeds two atlases and adds $\gamma L_{\text{seg}}$, where $L_{\text{seg}}$ is the mean Dice overlap of the warped atlas label maps. A second mechanism is spatial augmentation: each atlas and its label map are deformed by a smooth random field before training, which creates new atlas-like examples. At test time, the trained network warps the $N$ labeled atlases plus $N_I$ augmented copies, propagates their one-hot label probabilities, and fuses them by voxel-wise maximum likelihood.

What would settle it

Train the same MAS-SS and SegNet-DA pipelines on a new anatomy (or a different MRI protocol) using only the small labeled set, with no auxiliary unlabeled images for registration training, and measure the Dice gap relative to a fully supervised model; if the gap is substantially larger than four points at three atlases, the claim that few labels suffice would be refuted.

Watch

Extended reading notes

Core claim

Using 7,329 unlabeled brain MRI scans to train an unsupervised registration network and 18 labeled atlases (with 10 held out for test), the paper shows that the number of labeled atlases needed for accurate segmentation is small. In multi-atlas segmentation, warping one-hot label probabilities from one to seven atlases and fusing them yields Dice scores that approach the fully supervised upper bound; adding random deformation augmentation to the atlas images helps most when only one or two atlases are used. In the supervised paradigm, training the same encoder-decoder network on the few labeled atlases with random smooth deformations also reaches near-upper-bound Dice once three or more atlases are present. The proposed semi-supervised method, MAS-SS, adds a segmentation-overlap (Dice) term to the registration loss during a fraction of training iterations, using atlas-to-atlas pairs, and this consistently improves Dice over the unsupervised MAS baseline while keeping mean surface distance below 0.4 mm and maximum surface distance below 7 mm. The paper concludes that deep-learning segmentation does not inherently require many labeled scans and that the topology-preserving registration framework is especially beneficial for small anatomical structures and boundary accuracy.

Load-bearing premise

The comparison rests on having thousands of unlabeled brain MRI scans to train the registration network; if such an unlabeled pool is not available for a new segmentation problem, the near-upper-bound performance with few labeled atlases is not established.

Editorial extensions

If this is right

With only three labeled atlases, both multi-atlas segmentation and a data-augmented supervised network reach within four Dice points of a fully supervised model, so large annotation efforts are not automatically required for competitive brain MRI segmentation.
Adding a semi-supervised Dice term to registration training improves Dice over the unsupervised baseline at every atlas count, and the improvement is largest for small structures such as the amygdala and hippocampus.
Random smooth deformation augmentation alone makes supervised segmentation usable with very few labels, but it leaves larger surface distances than the registration-based approach.
For one or two atlases, the semi-supervised multi-atlas method is the better choice; the supervised method catches up in Dice at roughly three atlases but not in boundary accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An implicit consequence is that the scarce resource in few-label segmentation is unlabeled images rather than labels: the registration network is trained on thousands of unlabeled scans, so the recipe should transfer to any anatomy with a large unlabeled image pool.
A testable extension is that distribution shift between the unlabeled corpus and the target scans will widen the reported Dice gap; the current experiments use brain MRIs from several public datasets, so the four-point gap is likely optimistic for a new scanner or protocol.
Because the two paradigms differ more in surface distance than in Dice, boundary-sensitive applications such as surgical planning should prefer the semi-supervised multi-atlas method even when overlap scores look similar.
The same semi-supervised registration loss could be tested in other few-label segmentation domains, and a plausible prediction is that the topology-preserving advantage will persist wherever anatomy has consistent structure across subjects.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

The headline: this is a useful empirical study that squarely tests whether deep learning segmentation really needs large labeled sets in brain MRI. The answer, from their experiments, is that with a few labeled scans—even one—both multi-atlas segmentation with learning-based registration and a U-Net with random deformation augmentation land close to a fully supervised upper bound. That is a meaningful result for anyone working with small medical datasets.

What's actually new: the systematic comparison across N=1..7 labeled atlases for both paradigms, and the MAS-SS variant that adds occasional atlas-to-atlas Dice supervision to unsupervised registration plus random deformation augmentation. The components are known—VoxelMorph, weakly-supervised registration, U-Net style augmentation—but the combination is sensible and the evaluation is broader than prior work. The paper is honest that the registration network trains on 7,329 unlabeled scans; SegNet-DA gets its strength purely from augmentation on the few labeled examples. That nuance matters and they don't hide it. The citation pattern is appropriate; they build on the relevant literature rather than ignoring it.

Where it's soft: the quantitative claims are under-supported. They never state n, the number of random atlas-set permutations they averaged over. Figure 3 shows mean curves only, no error bars or variability bands. The test set is 10 subjects. With that sample, an 0.04 Dice gap at N=3 could plausibly be within noise, so the precise claim of "within four Dice points" is not established, though the qualitative trend is consistent. They also use the word "significantly" for surface distance without any significance test. The hyperparameters (gamma, lambda, 10% supervision frequency) are hand-tuned; they mention trying 10% vs 50% on a small scenario, which is fine but should be reported as model selection, and the validation set's role is unclear. All of these are fixable with better reporting, not deep flaws.

The unlabeled-data dependence is real but less damaging than it first appears, because SegNet-DA—the main competitor—does not use the 7,329 unlabeled scans, and the MAS comparison is about the registration network's training data, not the labels. The central hypothesis that few labeled atlases suffice is supported qualitatively.

Who it's for: people building segmentation pipelines for small labeled datasets, and researchers thinking about evaluation standards for deep learning in medical imaging. Worth a serious referee; I'd ask for error bars, n, and significance testing before accepting, but I wouldn't desk-reject it.

Recommendation: send to peer review, with requests for uncertainty quantification and a clearer description of the validation procedure.

Referee Report

3 major / 5 minor

Summary. The paper studies brain MRI segmentation when only N = 1..7 labeled atlases are available. It compares multi-atlas segmentation (MAS) with learning-based registration, including a proposed semi-supervised variant MAS-SS, against supervised CNNs (SegNet, SegNet-DA) and a fully supervised upper bound (SegNet-Full). The registration network is trained on 7,329 unlabeled T1 scans from eight public datasets, and the small labeled dataset is the Buckner40 set. The authors report that both paradigms achieve Dice scores within four points of the upper bound with only three atlases, that MAS-SS consistently improves over MAS/MAS-DA in Dice, and that MAS-SS yields significantly lower mean and maximum surface distances than SegNet-DA. The paper concludes that deep-learning segmentation does not necessarily require large labeled datasets.

Significance. The empirical finding is significant and potentially practice-relevant: if robust, it challenges the conventional assumption that CNN segmentation needs large labeled corpora and provides a systematic comparison of multi-atlas and supervised paradigms in the low-label regime. The paper has clear strengths: it sweeps N from 1 to 7, uses public datasets, reports two complementary metrics (Dice and surface distance), and proposes a simple semi-supervised registration variant that is a natural extension of voxel-based registration learning. The main risk is that the key quantitative claims currently lack explicit uncertainty quantification, so the strength of the conclusion exceeds what the reported numbers can support at this stage.

major comments (3)

[Section 3.1.3 and Figure 3] The number n of random atlas-set permutations is never reported, and Figure 3 shows only mean curves without error bars, confidence intervals, or significance tests. With a test set of only 10 subjects, the claims that the best methods are 'within four Dice points' of the upper bound and that MAS-SS is 'consistently' and 'significantly better' in surface distance may reflect only the particular atlas sets and test subjects chosen. Please report n explicitly, add per-N variability (e.g., shaded standard deviations or box plots over atlas-set permutations), and perform paired statistical tests (e.g., Wilcoxon signed-rank) for the claimed improvements.
[Sections 3.1.2 and 3.2] The favorable MAS results, including MAS-SS, depend on training the registration network with 7,329 unlabeled T1 scans from eight public datasets. Thus the title and abstract claim that 'few labeled atlases are necessary' is only valid when a large unlabeled image corpus is also available; without such unlabeled data, the proposed MAS-SS method cannot be trained as described. Please state this dependence prominently in the abstract and conclusion, and clarify whether SegNet-DA is the only method in the comparison that does not require unlabeled images.
[Section 2.4 and Section 3.1.3] The number NI of augmented atlases used at evaluation time is introduced in Section 2.4 but never specified in the experimental section. This value directly determines how many warped label maps are fused and therefore affects the reported MAS Dice and surface-distance numbers. Please report NI and the remaining training hyperparameters (number of iterations, learning rate, batch size, and the details of the 'small single scenario' used to select the 10% supervised-iteration frequency) so the experiments are reproducible.

minor comments (5)

[Section 3.1.3] There is a typo: 'Bucker40' should be 'Buckner40'.
[Section 3.2] The word 'acheive' should be 'achieve'.
[Abstract] The phrase 'These method' should be 'These methods'.
[Figure 5 and Section 3.1.3] The symbol n is used in Figure 5 for the number of atlases, while Section 3.1.3 uses N for the number of atlases and n for the number of random permutations; please use consistent notation.
[Section 3.1.2] Please clarify how many of the 7,829 scans in the eight public datasets have FreeSurfer segmentations used to train SegNet-Full, given that the same section states segmentations are 'only used for evaluation' and also that labels from these datasets are 'not used during training the registration network'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the paper is an empirical comparison with an external held-out test set and no fitted prediction that reduces to its inputs.

full rationale

The paper is an empirical study comparing multi-atlas segmentation and supervised segmentation in the few-labeled-atlas regime. The proposed MAS-SS training loss (Eq. 2) combines an NCC image-similarity term, a smoothness term, and a Dice term measuring overlap between two atlas label maps after warping; the evaluation, in contrast, measures Dice and surface distance of the final fused segmentation against manual annotations on a held-out Buckner40 test set (Section 3.1.2 and Section 3.1.5). The training objective is therefore not the test metric by construction, and no model parameter is fitted to the test set. The headline result, that with three atlases the best MAS and SegNet methods are within four Dice points of the fully supervised upper bound, is a measured outcome of the experiments, not a consequence of a self-referential definition. Author self-citations such as VoxelMorph [7] are used to borrow network architecture and an unsupervised registration loss, but the central comparison also includes SegNet variants and external manual segmentations, so the load-bearing empirical claim does not reduce to a self-citation. The absence of error bars and the unspecified number of random atlas-set permutations are statistical reporting concerns, not circularity. No step in the paper's derivation chain equates a prediction with its input by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on two domain assumptions: that a large unlabeled corpus (7,329 scans) can substitute for labeled data in training the registration network, and that random smooth deformations of a few labeled atlases produce realistic training variability. The method uses hand-tuned loss weights and a supervision frequency chosen on a small scenario. No new entities are postulated.

free parameters (4)

lambda (smoothness weight) = 1.5
Regularization weight for the smoothness loss, taken from prior work [7] and not re-tuned here.
gamma (Dice supervision weight) = 1.0
Weight for the supervised Dice loss in the semi-supervised registration objective (Eq. 2).
supervised iteration frequency = 10%
Fraction of training iterations that use atlas-to-atlas supervision; authors compared 50% vs 10% on a small scenario and chose 10% (Section 3.1.4). This is tuned to the data.
random deformation augmentation parameters = not specified
The distribution of random smooth deformations used for data augmentation (Section 2.3) is not quantified, so the augmentation strength is an unspecified free parameter.

assumptions (4)

domain assumption Unlabeled brain MRI scans from eight public datasets are representative of the Buckner40 test distribution for training the registration network.
Section 3.1.2 uses 7,329 unlabeled scans from ADNI, OASIS, ABIDE, etc. to train the registration network; the method's success with few labeled atlases depends on this unlabeled corpus.
domain assumption Random smooth deformations of the few labeled atlases generate realistic training examples.
Section 2.3 augments atlases with random deformation fields; the supervised SegNet-DA and the MAS-DA results depend on this assumption that synthesized deformations capture test-time variability.
domain assumption Dice overlap between atlas segmentation maps is a valid supervisory signal for image registration, equivalent to anatomical correspondence.
Section 2.2 introduces the Dice-based Lseg term (Eq. 3) in the registration loss, assuming that maximizing label overlap yields correct deformation fields.
domain assumption Architecture and hyperparameters from VoxelMorph transfer to the few-atlas setting.
Section 3.1.4 sets network architecture and parameters based on prior literature [7]; no re-tuning for the few-label regime beyond the 10% frequency.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation." pith.science (2026). https://pith.science/paper/3BZ65UZQ

@misc{pith2026190804466,
  author       = {Pith},
  title        = {Pith review of: Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3BZ65UZQ}},
  note         = {Machine review of arXiv:1908.04466}
}

read the original abstract

We tackle biomedical image segmentation in the scenario of only a few labeled brain MR images. This is an important and challenging task in medical applications, where manual annotations are time-consuming. Current multi-atlas based segmentation methods use image registration to warp segments from labeled images onto a new scan. In a different paradigm, supervised learning-based segmentation strategies have gained popularity. These method consistently use relatively large sets of labeled training data, and their behavior in the regime of a few labeled biomedical images has not been thoroughly evaluated. In this work, we provide two important results for segmentation in the scenario where few labeled images are available. First, we propose a straightforward implementation of efficient semi-supervised learning-based registration method, which we showcase in a multi-atlas segmentation framework. Second, through an extensive empirical study, we evaluate the performance of a supervised segmentation approach, where the training images are augmented via random deformations. Surprisingly, we find that in both paradigms, accurate segmentation is generally possible even in the context of few labeled images.

Figures

Figures reproduced from arXiv: 1908.04466 by the authors.

**Figure 2.** CNN architecture for image registration. Each rectangle represents a 3D volume, the number inside the box indicates the number of filters, and the spatial resolution relative to the original size is included below each rectangle. We also analyze supervised segmentation strategies. Recent supervised learning-based segmentation methods use a discriminative CNN model fθ(I) = S that maps images I to their segmentation m… view at source ↗

**Figure 3.** Dice score and surface distance of test scans for various segmentation methods. Upper: [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Dice score of each segmentation method across various brain structures. We shorten white [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Examples of MR slices with segmentation for several test subjects. We indicate the numbers [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

39 extracted references · 38 canonical work pages

[1]

The adhd-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience

The ADHD Consortium. The adhd-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience, 6, 09 2012

work page 2012
[2]

Deep learning for brain mri segmentation: State of the art and future directions

Zeynettin Akkus, Alﬁia Galimzianova, Assaf Hoogi, and Rubin. Deep learning for brain mri segmentation: State of the art and future directions. Journal of Digital Imaging, pages 1–11, 6 2017

work page 2017
[3]

Artaechevarria, A

X. Artaechevarria, A. Munoz-Barrutia, and C. Ortiz-de-Solorzano. Combination strategies in multi-atlas image segmentation: Application to brain mr data. IEEE Transactions on Medical Imaging, 28(8):1266–1277, Aug 2009

work page 2009
[4]

A fast diffeomorphic image registration algorithm

John Ashburner. A fast diffeomorphic image registration algorithm. Neuroimage, 38(1):95–113, 2007

work page 2007
[5]

Symmetric diffeo- morphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

Brian B Avants, Charles L Epstein, Murray Grossman, and James C Gee. Symmetric diffeo- morphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis, 12(1):26–41, 2008

work page 2008
[6]

Multiresolution elastic matching

Ruzena Bajcsy and Stane Kovaˇciˇc. Multiresolution elastic matching. Computer vision, graphics, and image processing, 46(1):1–21, 1989

work page 1989
[7]

Guha Balakrishnan, Amy Zhao, Mert Sabuncu, John Guttag, and Adrian V . Dalca. V oxelmorph: A learning framework for deformable medical image registration. IEEE TMI, 2019. 7

work page 2019
[8]

Computing large deforma- tion metric mappings via geodesic ﬂows of diffeomorphisms

M Faisal Beg, Michael I Miller, Alain Trouvé, and Laurent Younes. Computing large deforma- tion metric mappings via geodesic ﬂows of diffeomorphisms. International journal of computer vision, 61(2):139–157, 2005

work page 2005

Show all 39 references

[9]

Semi- supervised and task-driven data augmentation

Krishna Chaitanya, Neerav Karani, Christian Baumgartner, and Ender Konukoglu. Semi- supervised and task-driven data augmentation. arXiv preprint arXiv:1902.05396, 2019

1902 arXiv
[10]

Harvard aging brain study: dataset and accessibility

Alexander Dagley, Molly LaPoint, Willem Huijbers, Trey Hedden, Donald G McLaren, Jas- meer P Chatwal, Kathryn V Papp, Rebecca E Amariglio, Deborah Blacker, Dorene M Rentz, et al. Harvard aging brain study: dataset and accessibility. NeuroImage, 2017

2017
[11]

Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces

Adrian V Dalca, Guha Balakrishnan, John Guttag, and Mert R Sabuncu. Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Medical Image Analysis, 2019

2019
[12]

Patch-based discrete registration of clinical brain images

Adrian V Dalca, Andreea Bobu, Natalia S Rost, and Polina Golland. Patch-based discrete registration of clinical brain images. International Workshop on Patch-based Techniques in Medical Imaging, (60–67), 2016

2016
[13]

Anatomical priors in convolutional networks for unsupervised biomedical segmentation

Adrian V Dalca, John Guttag, and Mert R Sabuncu. Anatomical priors in convolutional networks for unsupervised biomedical segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9290–9299, 2018

2018
[14]

A deep learning framework for unsupervised afﬁne and deformable image registration

Bob de V os, Floris F Berendsen, Max A Viergever, Hessam Sokooti, Marius Staring, and Ivana Išgum. A deep learning framework for unsupervised afﬁne and deformable image registration. Medical image analysis, 52:128–143, 2019

2019
[15]

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Adriana Di Martino, Chao-Gan Yan, Qingyang Li, Erin Denio, Francisco X Castellanos, Kaat Alaerts, Jeffrey S Anderson, Michal Assaf, Susan Y Bookheimer, Mirella Dapretto, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain arch...

2014
[16]

L. R. Dice. Measures of the amount of ecologic association between species. Ecology, 1945

1945
[17]

B. Fischl. Freesurfer. Neuroimage, 62(2), 774-781., 2012

2012
[18]

The mcic collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia

Randy L Gollub, Jody M Shoemaker, Margaret D King, White, et al. The mcic collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics, 11(3):367–388, 2013

2013
[19]

Automatic brain tumor detection and segmentation using u-net based fully convolutional networks

Dong Hao, Yang Guang, Liu Fangde, Mo Yuanhan, and Guo Yike. Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. annual conference on medical image understanding and analysis, pages 506–517, 2017

2017
[20]

Brain genomics superstruct project initial data release with structural, functional, and behavioral measures

Avram J Holmes, Marisa O Hollinshead, Timothy M O’Keefe, et al. Brain genomics superstruct project initial data release with structural, functional, and behavioral measures. Scientiﬁc data, 2015

2015
[21]

Weakly-supervised convolutional neural networks for multimodal image registration

Yipeng Hu, Marc Modat, Eli Gibson, Wenqi Li, Nooshin Ghavami, Ester Bonmati, Guotai Wang, Steven Bandula, Caroline M Moore, Mark Emberton, et al. Weakly-supervised convolutional neural networks for multimodal image registration. Medical image analysis, 2018

2018
[22]

Differential data augmenta- tion techniques for medical imaging classiﬁcation tasks

Zeshan Hussain, Francisco Gimenez, Darvin Yi, and Daniel Rubin. Differential data augmenta- tion techniques for medical imaging classiﬁcation tasks. Annual Symposium proceedings. AMIA Symposium, 2017:979–984, 04 2018

2017
[23]

Multi-atlas segmentation of biomedical images: a survey

Juan Eugenio Iglesias and Mert R Sabuncu. Multi-atlas segmentation of biomedical images: a survey. Medical image analysis, 24(1):205–219, 2015

2015
[24]

Deep multi-class segmentation without ground-truth labels

Thomas Joyce, Agisilaos Chartsias, and Sotirios A Tsaftaris. Deep multi-class segmentation without ground-truth labels. International Conference on Medical Imaging with Deep Learning, 2018

2018
[25]

K Kamnitsas, C Ledig, V . F. Newcombe, et al. Efﬁcient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis, (36):61–78, 2017. 8

2017
[26]

Cnn-based segmentation of medical imaging data

Baris Kayalibay, Grady Jensen, and Patrick van der Smagt. Cnn-based segmentation of medical imaging data. arXiv preprint arXiv:1701.03056, 2017

2017 arXiv
[27]

Evaluation of 14 nonlinear deformation algorithms applied to human brain mri registration

Arno Klein, Jesper Andersson, A Ardekani, et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain mri registration. NeuroImage, 46:786–802, 02 2009

2009
[28]

L. M. Koch, M. Rajchl, W. Bai, C. F. Baumgartner, T. Tong, J. Passerat-Palmbach, P. Aljabar, and D. Rueckert. Multi-atlas segmentation using partially annotated data: Methods and annotation strategies. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(7):1683–...

2018
[29]

Learn- ing a probabilistic model for diffeomorphic registration

Julian Krebs, Hervé e Delingette, Boris Mailhé, Nicholas Ayache, and Tommaso Mansi. Learn- ing a probabilistic model for diffeomorphic registration. IEEE transactions on medical imaging, 2019

2019
[30]

Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults

Daniel S Marcus, Tracy H Wang, Jamie Parker, John G Csernansky, John C Morris, and Randy L Buckner. Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience, 19(9):1...

2007
[31]

The parkinson progression marker initiative (ppmi)

Kenneth Marek, Danna Jennings, Shirley Lasch, et al. The parkinson progression marker initiative (ppmi). Progress in neurobiology, 2011

2011
[32]

V-net: Fully convolutional neural networks for volumetric medical image segmentation

Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. Fourth International Conference on 3D Vision, pages 565–571, 2016

2016
[33]

Ways toward an early diagnosis in alzheimer’s disease: the alzheimer’s disease neuroimaging initiative (adni)

Susanne G Mueller, Michael W Weiner, Thal, et al. Ways toward an early diagnosis in alzheimer’s disease: the alzheimer’s disease neuroimaging initiative (adni). Alzheimer’s & Dementia, 1(1):55–66, 2005

2005
[34]

Pereira, A

S. Pereira, A. Pinto, V . Alves, and C. A. Silva. Brain tumor segmentation using convolutional neural networks in mri images. IEEE Transactions on Medical Imaging, 35(5):1240–1251, May 2016

2016
[35]

U-net: Convolutional networks for biomedical image segmentation

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. MICAAI, pages 234–241, 2015

2015
[36]

A generative model for image segmenta- tion based on label fusion

Mert R Sabuncu, BT Thomas Yeo, Van Leemput, et al. A generative model for image segmenta- tion based on label fusion. IEEE TMI, 29(10):1714–1729, 2010

2010
[37]

Multi-atlas segmentation with joint label fusion

Hongzhi Wang, Jung W Suh, Sandhitsu R Das, John B Pluta, Caryne Craige, and Paul A Yushkevich. Multi-atlas segmentation with joint label fusion. IEEE Trans Pattern Anal Mach Intell, 35(3):611–623, Mar 2013

2013
[38]

Quicksilver: Fast predictive image registration–a deep learning approach

Xiao Yang, Roland Kwitt, Martin Styner, and Marc Niethammer. Quicksilver: Fast predictive image registration–a deep learning approach. NeuroImage, 158:378–396, 2017

2017
[39]

Data augmentation using learned transforms for one-shot medical image segmentation

Amy Zhao, Guha Balakrishnan, Frédo Durand, John V Guttag, and Adrian V Dalca. Data augmentation using learned transforms for one-shot medical image segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019. 9

2019

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

The adhd-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience

The ADHD Consortium. The adhd-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience, 6, 09 2012

work page 2012

[2] [2]

Deep learning for brain mri segmentation: State of the art and future directions

Zeynettin Akkus, Alﬁia Galimzianova, Assaf Hoogi, and Rubin. Deep learning for brain mri segmentation: State of the art and future directions. Journal of Digital Imaging, pages 1–11, 6 2017

work page 2017

[3] [3]

Artaechevarria, A

X. Artaechevarria, A. Munoz-Barrutia, and C. Ortiz-de-Solorzano. Combination strategies in multi-atlas image segmentation: Application to brain mr data. IEEE Transactions on Medical Imaging, 28(8):1266–1277, Aug 2009

work page 2009

[4] [4]

A fast diffeomorphic image registration algorithm

John Ashburner. A fast diffeomorphic image registration algorithm. Neuroimage, 38(1):95–113, 2007

work page 2007

[5] [5]

Symmetric diffeo- morphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

Brian B Avants, Charles L Epstein, Murray Grossman, and James C Gee. Symmetric diffeo- morphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis, 12(1):26–41, 2008

work page 2008

[6] [6]

Multiresolution elastic matching

Ruzena Bajcsy and Stane Kovaˇciˇc. Multiresolution elastic matching. Computer vision, graphics, and image processing, 46(1):1–21, 1989

work page 1989

[7] [7]

Guha Balakrishnan, Amy Zhao, Mert Sabuncu, John Guttag, and Adrian V . Dalca. V oxelmorph: A learning framework for deformable medical image registration. IEEE TMI, 2019. 7

work page 2019

[8] [8]

Computing large deforma- tion metric mappings via geodesic ﬂows of diffeomorphisms

M Faisal Beg, Michael I Miller, Alain Trouvé, and Laurent Younes. Computing large deforma- tion metric mappings via geodesic ﬂows of diffeomorphisms. International journal of computer vision, 61(2):139–157, 2005

work page 2005