Learning Topological Interactions for Multi-Class Medical Image Segmentation
This addresses the limitation of deep learning methods in capturing crucial topological constraints in biomedical images, which is important for medical professionals and researchers, though it appears incremental as it builds on existing segmentation frameworks.
The paper tackles the problem of encoding topological interactions like containment and exclusion in multi-class medical image segmentation, introducing a novel topological interaction module that improves segmentation quality across various datasets and modalities.
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