End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction

Tal Shaharabany; Lior Wolf

doi:https://doi.org/10.1007/978-3-031-16443-9_30

End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction

Tal Shaharabany, Lior Wolf

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The leading medical image segmentation methods represent the output map as a pixel grid. We present an alternative in which the object edges are modeled, per image patch, as a polygon with k vertices that is coupled with per-patch label probabilities. The vertices are optimized by employing a differentiable neural renderer to create a raster image. The delineated region is then compared with the ground truth segmentation. Our method obtains multiple state-of-the-art results for the Gland segmentation dataset (Glas), the Nucleus challenges (MoNuSeg), and multiple polyp segmentation datasets, as well as for non-medical benchmarks, including Cityscapes, CUB, and Vaihingen. Our code for training and reproducing these results is attached as a supplement.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
Editors	Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	308-318
Number of pages	11
ISBN (Print)	9783031164422
DOIs	https://doi.org/10.1007/978-3-031-16443-9_30
State	Published - 2022
Event	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 Sep 2022 → 22 Sep 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13435 LNCS

Conference

Conference	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	18/09/22 → 22/09/22

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

https://doi.org/10.1007/978-3-031-16443-9_30

Cite this

Shaharabany, T., & Wolf, L. (2022). End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings (pp. 308-318). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13435 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16443-9_30

Shaharabany, Tal ; Wolf, Lior. / End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. editor / Linwei Wang ; Qi Dou ; P. Thomas Fletcher ; Stefanie Speidel ; Shuo Li. Springer Science and Business Media Deutschland GmbH, 2022. pp. 308-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The leading medical image segmentation methods represent the output map as a pixel grid. We present an alternative in which the object edges are modeled, per image patch, as a polygon with k vertices that is coupled with per-patch label probabilities. The vertices are optimized by employing a differentiable neural renderer to create a raster image. The delineated region is then compared with the ground truth segmentation. Our method obtains multiple state-of-the-art results for the Gland segmentation dataset (Glas), the Nucleus challenges (MoNuSeg), and multiple polyp segmentation datasets, as well as for non-medical benchmarks, including Cityscapes, CUB, and Vaihingen. Our code for training and reproducing these results is attached as a supplement.",

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Shaharabany, T & Wolf, L 2022, End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction. in L Wang, Q Dou, PT Fletcher, S Speidel & S Li (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13435 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 308-318, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore, 18/09/22. https://doi.org/10.1007/978-3-031-16443-9_30

End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction. / Shaharabany, Tal; Wolf, Lior.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. ed. / Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li. Springer Science and Business Media Deutschland GmbH, 2022. p. 308-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13435 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Shaharabany T, Wolf L. End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction. In Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 308-318. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-031-16443-9_30

End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction

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