Learning Topological Interactions for Multi-Class Medical Image Segmentation

Saumya Gupta, Xiaoling Hu, James Kaan, Michael Jin, Mutshipay Mpoy, Katherine Chung, Gagandeep Singh, Mary Saltz, Tahsin Kurc, Joel Saltz, Apostolos Tassiopoulos, Prateek Prasanna, Chao Chen

ECCV 2022

doi:10.1007/978-3-031-19818-2_40 /eccv/2022/gupta2022eccv-learning/

Abstract

Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions among different classes (e.g., containment and exclusion). These constraints naturally arise in biomedical images and can be crucial in improving segmentation quality. In this paper, we introduce a novel topological interaction module to encode the topological interactions into a deep neural network. The implementation is completely convolution-based and thus can be very efficient. This empowers us to incorporate the constraints into end-to-end training and enrich the feature representation of neural networks. The efficacy of the proposed method is validated on different types of interactions. We also demonstrate the generalizability of the method on both proprietary and public challenge datasets, in both 2D and 3D settings, as well as across different modalities such as CT and Ultrasound. Code is available at: https://github.com/TopoXLab/TopoInteraction

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Cite

Text

Gupta et al. "Learning Topological Interactions for Multi-Class Medical Image Segmentation." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-19818-2_40

Markdown

[Gupta et al. "Learning Topological Interactions for Multi-Class Medical Image Segmentation." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/gupta2022eccv-learning/) doi:10.1007/978-3-031-19818-2_40

BibTeX

@inproceedings{gupta2022eccv-learning,
  title     = {{Learning Topological Interactions for Multi-Class Medical Image Segmentation}},
  author    = {Gupta, Saumya and Hu, Xiaoling and Kaan, James and Jin, Michael and Mpoy, Mutshipay and Chung, Katherine and Singh, Gagandeep and Saltz, Mary and Kurc, Tahsin and Saltz, Joel and Tassiopoulos, Apostolos and Prasanna, Prateek and Chen, Chao},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2022},
  doi       = {10.1007/978-3-031-19818-2_40},
  url       = {https://mlanthology.org/eccv/2022/gupta2022eccv-learning/}
}