Explicit topological priors for deep-learning based image segmentation using persistent homology

Edelsbrunner, H., Letscher, D., Zomorodian, A.: Topological persistence and simplification. In: Foundations of Computer Science. pp. 454–463. IEEE (2000)

2000

Earlier work this paper cites.

Kaczynski, T., Mischaikow, K., Mrozek, M.: Computational homology, vol. 157. Springer Science & Business Media (2006)

2006

Earlier work this paper cites.

Edelsbrunner, H., Harer, J.: Persistent homology-a survey. Contemporary mathematics 453

2008

Earlier work this paper cites.

Ghrist, R.: Barcodes: the persistent topology of data. Bulletin of the American Mathematical Society 45

2008

Earlier work this paper cites.

Wagner, H., Chen, C., Vuçini, E.: Efficient computation of persistent homology for cubical data. Topological methods in data analysis and visualization II pp. 91–106 (2012)

Original

2012

Earlier work this paper cites.

Gao, M., Chen, C., Zhang, S., Qian, Z., Metaxas, D., Axel, L.: Segmenting the papillary muscles and the trabeculae from high resolution cardiac CT through restoration of topological handles. In: IPMI. pp. 184–195. Springer (2013)

2013

Earlier work this paper cites.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

Original

2014

Earlier work this paper cites.

GUDHI User and Reference Manual (2015), http://gudhi.gforge.inria.fr

2015

Cited alongside, same era.

Funke, J., Hamprecht, F.A., Zhang, C.: Learning to segment: Training hierarchical segmentation under a topological loss. In: MICCAI. pp. 268–275. Springer (2015)

2015

Cited alongside, same era.

Petersen, S.E., Matthews, P.M., Francis, J.M., Robson, M.D., Zemrak, F., Boubertakh, R., Young, A.A., Hudson, S., Weale, P., Garratt, S., Collins, R.: UK Biobank’s cardiovascular magnetic resonance protocol. Journal of cardiovascular magnetic resonance 18

2015

Cited alongside, same era.

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI. pp. 234–241. Springer (2015)

2015

Cited alongside, same era.

Bai, W., Oktay, O., Sinclair, M., Suzuki, H., Rajchl, M., Tarroni, G., Glocker, B., King, A., Matthews, P.M., Rueckert, D.: Semi-supervised learning for network-based cardiac mr image segmentation. In: MICCAI. pp. 253–260. Springer (2017)

2017

Later among the works it cites.

Otter, N., Porter, M.A., Tillmann, U., Grindrod, P., Harrington, H.A.: A roadmap for the computation of persistent homology. EPJ Data Science 6

2017

Later among the works it cites.

Chen, C., Ni, X., Bai, Q., Wang, Y.: TopoReg: A Topological Regularizer for Classifiers. arXiv 1806.10714 (2018)

2018

Later among the works it cites.

Ganaye, P.A., Sdika, M., Benoit-Cattin, H.: Semi-supervised Learning for Segmentation Under Semantic Constraint. In: MICCAI (2018)

2018

Later among the works it cites.

Mosinska, A., Marquez-Neila, P., Kozinski, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: CVPR (2018)