TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
Open accessThe 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.
Integrity signals
Registry
Cites
Statistics
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.
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?
TEDS-Net requires explicit prior knowledge of the anatomical topology.
“TEDS-Net requires explicit prior knowledge of the anatomical topology.”
From rNCA: Self-Repairing Segmentation Masks (2025) · cites this paper as [10]
TEDS-Net is a spatial transformer network that learns a topology-preserving deformation field and warps a prior shape of known topology to each input.
“The second is TEDS-Net [10], a spatial transformer network that learns a topology-preserving deformation field and warps a prior shape of known topology to each input.”
From rNCA: Self-Repairing Segmentation Masks (2025) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 20 · Cited by 22
Research works printed but not linked: 15
- [1]1. Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-euclidean polyaffine framework for locally rigid or affine registration. In: International Workshop on Biomedical Image Registration. pp. 120-127. Springer (2006)
- [2]2. Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38 (1), 95-113 (2007)
- [3]3. Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE transactions on medical imaging 38 (8), 1788-1800 (2019)
- [4]4. Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging 37 (11), 2514-2525 (2018)
- [5]5. Clough, J., Byrne, N., Oksuz, I., Zimmer, V.A., Schnabel, J.A., King, A.: A topological loss function for deep-learning based image segmentation using persistent homology. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)
- [6]6. Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 729-738. Springer (2018)
- [8]8. Dinsdale, N.K., Jenkinson, M., Namburete, A.I.: Spatial warping network for 3d segmentation of the hippocampus in mr images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 284-291. Springer (2019)
- [9]9. Hu, X., Fuxin, L., Samaras, D., Chen, C.: Topology-preserving deep image segmentation. arXiv preprint arXiv:1906.05404 (2019) · arXiv
- [10]10. Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. Advances in neural information processing systems 28 , 2017-2025 (2015)
- [12]12. Millson, J.J.: On the first betti number of a constant negatively curved manifold. Annals of Mathematics pp. 235-247 (1976)
- [13]13. Moler, C., Van Loan, C.: Nineteen dubious ways to compute the exponential of a matrix, twenty-five years later. SIAM review 45 (1), 3-49 (2003)
- [14]14. Mosinska, A., Marquez-Neila, P., Kozi´ nski, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3136-3145 (2018)
- [16]16. Stergios, C., Mihir, S., Maria, V., Guillaume, C., Marie-Pierre, R., Stavroula, M., Nikos, P.: Linear and deformable image registration with 3d convolutional neural networks. In: Image Analysis for Moving Organ, Breast, and Thoracic Images, pp. 13-22. Springer (2018)
- [18]18. Wickramasinghe, U., Knott, G., Fua, P.: Probabilistic atlases to enforce topological constraints. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 218-226. Springer (2019)
- [19]19. Zeng, Q., Karimi, D., Pang, E.H., Mohammed, S., Schneider, C., Honarvar, M., Salcudean, S.E.: Liver segmentation in magnetic resonance imaging via mean shape fitting with fully convolutional neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 246-254. Springer (2019)
- Authors
- Wyburd, M. K., Dinsdale, N. K., Namburete, A. I. L., & Jenkinson, M.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Wyburd, Madeleine K.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 250-260
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3184326996
- Cited by
- 22
- References
- 20
- Citations by year
- 2025: 5 · 2024: 9 · 2023: 3 · 2022: 4 · 2021: 1
- FWCI
- 1.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Wellcome Trust (L016052, 203139/Z/16/Z, 215573, 215573/Z/19/Z, EP/L016052/1, 203139, 203139/Z/16)
- National Institute for Health and Care Research (203139/Z/16/Z)
- Royal Academy of Engineering (203139/Z/16/Z)
- Medical Research Council (203139/Z/16/Z, EP/L016052/1)
- Engineering and Physical Sciences Research Council (EP/L016052/1, EP/L016052, 203139/Z/16/Z)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- topology preservation, anatomical segmentation, diffeomorphisms, spatial transformer network, myocardium segmentation
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
