Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 430

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation

Zhao, Amy, Balakrishnan, Guha, Durand, Frédo, Guttag, John V., Dalca, Adrian V.·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2019.00874
Open this paper in CoChat — free account

Abstract

Image segmentation is an important task in many medical applications. Methods based on convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on supervised training with large labeled datasets. Labeling medical images requires significant expertise and time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. We present an automated data augmentation method for synthesizing labeled medical images. We demonstrate our method on the task of segmenting magnetic resonance imaging (MRI) brain scans. Our method requires only a single segmented scan, and leverages other unlabeled scans in a semi-supervised approach. We learn a model of transformations from the images, and use the model along with the labeled example to synthesize additional labeled examples. Each transformation is comprised of a spatial deformation field and an intensity change, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures. We show that training a supervised segmenter with these new examples provides significant improvements over state-of-the-art methods for one-shot biomedical image segmentation.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/cvpr.2019.00874

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 23 references

Research works printed but not linked: 56

  1. 10.1007/978-3-319-02126-3_3 · DOI
  2. 10.1007/978-3-319-66182-7_27 · DOI · Paper page
  3. 10.1109/TMI.2002.803111 · DOI · Paper page
  4. 10.1016/j.neuron.2007.10.015 · DOI · Paper page
  5. vincent, 2012, Fully automatic segmentation of the prostate using active appearance models, MICCAI Prostate MR Image Segmentat Challenge
  6. 10.1109/42.845174 · DOI · Paper page
  7. 10.1109/CVPR.2016.423 · DOI · Paper page
  8. 10.1023/A:1008074226832 · DOI
  9. 10.1109/42.511747 · DOI · Paper page
  10. 10.1038/sdata.2015.31 · DOI · Paper page
  11. 10.1016/j.cmpb.2004.01.002 · doi:10.1016/j.cmpb.2004.01.002
  12. hauberg, 2016, Dreaming more data: Class-dependent distributions over diffeomorphisms for learned data augmentation, Artificial Intelligence and Statistics, 342
  13. 10.1007/s12021-013-9184-3 · DOI · Paper page
  14. 10.1109/CVPR.2017.228 · DOI · Paper page
  15. hussain, 2017, Differential data augmentation techniques for medical imaging classification tasks, AMIA Annual Symposium Proceedings, 2017, 979
  16. 10.1016/j.neuroimage.2011.08.035 · DOI · Paper page
  17. 10.1109/ISBI.2015.7163826 · DOI · Paper page
  18. 10.1007/978-3-319-24553-9_68 · DOI · Paper page
  19. 10.1007/978-3-319-66179-7_27 · DOI
  20. ganaye, 2018, Semisupervised learning for segmentation under semantic constraint, International Conference on Medical Image Computing and Computer-Assisted Intervention, 595
  21. 10.1109/TMI.2010.2050897 · DOI · Paper page
  22. dalca, 2018, Unsupervised learning for fast probabilistic diffeomorphic registration, International Conference on Medical Image Computing and Computer-Assisted Intervention, 729
  23. 10.1109/42.811271 · DOI · Paper page
  24. 10.1109/CVPR.2018.00968 · DOI
  25. dong, 2018, Few-shot semantic segmentation with prototype learning, BMVC, 3, 4
  26. 10.2307/1932409 · DOI · Paper page
  27. eaton-rosen, 2018, Improving data augmentation for medical image segmentation, International Conference on Medical Imaging with Deep Learning
  28. 10.1109/CVPR.2000.855856 · DOI · Paper page
  29. 10.1093/brain/124.11.2232 · DOI · Paper page
  30. 10.1007/s11263-014-0731-7 · DOI
  31. 10.1016/j.neuroimage.2011.02.046 · DOI · Paper page
  32. 10.1109/ENBENG.2017.7889443 · DOI
  33. 10.1016/j.jalz.2005.06.003 · DOI · Paper page
  34. 10.1109/TMI.2002.804425 · DOI · Paper page
  35. 10.1023/B:VISI.0000043755.93987.aa · DOI · Paper page
  36. 10.1109/CVPR.2017.565 · DOI · Paper page
  37. chollet, 2015, Keras
  38. 10.1016/j.media.2009.02.008 · doi:10.1016/j.media.2009.02.008
  39. coelho de castro, 2018, Nonparametric density flows for mri intensity normalisation, International Conference on Medical Image Computing and Computer Assisted Intervention, 206
  40. 10.1007/3-540-48714-X_24 · DOI
  41. 10.1007/978-3-319-59050-9_44 · DOI
  42. 10.1117/12.431093 · DOI · Paper page
  43. 10.1109/CVPR.2018.00160 · DOI
  44. 10.1016/j.neuroimage.2015.03.069 · DOI · Paper page
  45. 10.1016/S1361-8415(01)00039-1 · DOI
  46. 10.1016/j.media.2007.06.004 · DOI · Paper page
  47. 10.1016/S0734-189X(89)80014-3 · DOI · Paper page
  48. milham, 2012, The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience, Frontiers in Systems Neuroscience, 6
  49. 10.1109/TMI.2019.2897538 · DOI · Paper page
  50. 10.1162/jocn.2007.19.9.1498 · DOI · Paper page
  51. 10.1016/j.neuroimage.2009.12.059 · DOI · Paper page
  52. 10.1038/mp.2013.78 · DOI · Paper page
  53. 10.1016/j.pneurobio.2011.09.005 · DOI · Paper page
  54. krebs, 2017, Robust non-rigid registration through agentbased action learning, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 344
  55. 10.1016/j.neuroimage.2004.09.016 · DOI
  56. 10.1109/TPAMI.2006.34 · DOI · Paper page

Cited by

See all 430

Metadata

Authors
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J. V., & Dalca, A. V.
Venue
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2019-06
Pages
8535-8545
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2019.00874
OpenAlex
W2964250774
Cited by
430
References
23
FWCI
21.00
Type
conference paper
Language
en
Open access
closed
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation. https://app.cochat.ai/papers/10.1109/cvpr.2019.00874
The paper
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J. V., & Dalca, A. V. (2019). Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00874

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.