Difference between revisions of "Publications/movn.22.nips"
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| lrdenewsdate = 2022-10-25 |
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methods, does not require any post-processing stage). The |
methods, does not require any post-processing stage). The |
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code of CavityLoss is freely available at |
code of CavityLoss is freely available at |
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− | \url<nowiki>{</nowiki>https://github.com/onvungocminh/CavityLoss<nowiki>}</nowiki><nowiki>}</nowiki> |
+ | \url<nowiki>{</nowiki>https://github.com/onvungocminh/CavityLoss<nowiki>}</nowiki><nowiki>}</nowiki>, |
+ | nodoi = <nowiki>{</nowiki><nowiki>}</nowiki> |
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<nowiki>}</nowiki> |
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Latest revision as of 19:08, 7 April 2023
- Authors
- Minh Ôn Vũ Ngoc, Nicolas Boutry, Jonathan Fabrizio
- Where
- 36th Conference on Neural Information Processing SystemsAI for Science Workshop
- Type
- inproceedings
- Projects
- Olena
- Keywords
- Image
- Date
- 2022-10-25
Abstract
The study of genetic and molecular mechanisms underlying tissue morphogenesis has received a lot of attention in biology. Especially, accurate segmentation of tissues into individual cells plays an important role for quantitative analyzing the development of the growing organs. Howeverinstance cell segmentation is still a challenging task due to the quality of the image and the fine-scale structure. Any small leakage in the boundary prediction can merge different cells together, thereby damaging the global structure of the image. In this paper, we propose an end-to-end topology-aware 3D segmentation method for plant tissues. The strength of the method is that it takes care of the 3D topology of segmented structures. The keystone is a training phase and a new topology-aware loss - the CavityLoss - that are able to help the network to focus on the topological errors to fix them during the learning phase. The evaluation of our method on both fixed and live plant organ datasets shows that our method outperforms state-of-the-art methods (and contrary to state-of-the-art methods, does not require any post-processing stage). The code of CavityLoss is freely available at https://github.com/onvungocminh/CavityLoss
Documents
Bibtex (lrde.bib)
@InProceedings{ movn.22.nips, author = {Minh \^On V\~{u} Ng\d{o}c and Nicolas Boutry and Jonathan Fabrizio}, title = {Topology-Aware Method to Segment {3D} Plan Tissue Images}, booktitle = {36th Conference on Neural Information Processing Systems, AI for Science Workshop}, year = 2022, abstract = {The study of genetic and molecular mechanisms underlying tissue morphogenesis has received a lot of attention in biology. Especially, accurate segmentation of tissues into individual cells plays an important role for quantitative analyzing the development of the growing organs. However, instance cell segmentation is still a challenging task due to the quality of the image and the fine-scale structure. Any small leakage in the boundary prediction can merge different cells together, thereby damaging the global structure of the image. In this paper, we propose an end-to-end topology-aware 3D segmentation method for plant tissues. The strength of the method is that it takes care of the 3D topology of segmented structures. The keystone is a training phase and a new topology-aware loss - the CavityLoss - that are able to help the network to focus on the topological errors to fix them during the learning phase. The evaluation of our method on both fixed and live plant organ datasets shows that our method outperforms state-of-the-art methods (and contrary to state-of-the-art methods, does not require any post-processing stage). The code of CavityLoss is freely available at \url{https://github.com/onvungocminh/CavityLoss}}, nodoi = {} }