Learning Probabilistic Structural Representation for Biomedical Image Segmentation

• URL: http://arxiv.org/abs/2206.01742v1
• Date: Fri, 3 Jun 2022 06:00:26 GMT
• Title: Learning Probabilistic Structural Representation for Biomedical Image Segmentation
• Authors: Xiaoling Hu, Dimitris Samaras and Chao Chen
• Abstract summary: We propose the first deep learning method to learn a structural representation. We empirically demonstrate the strength of our method, i.e., generating true structures rather than pixel-maps with better topological integrity.
• Score: 37.07198480786721
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate segmentation of various fine-scale structures from biomedical images is a very important yet challenging problem. Existing methods use topological information as an additional training loss, but are ultimately learning a pixel-wise representation. In this paper, we propose the first deep learning method to learn a structural representation. We use discrete Morse theory and persistent homology to construct an one-parameter family of structures as the structural representation space. Furthermore, we learn a probabilistic model that can do inference tasks on such a structural representation space. We empirically demonstrate the strength of our method, i.e., generating true structures rather than pixel-maps with better topological integrity, and facilitating a human-in-the-loop annotation pipeline using the sampling of structures and structure-aware uncertainty.

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