Topology-Preserving Image Segmentation with Spatial-Aware Persistent Feature Matching
This work addresses the challenge of improving topological correctness in segmentation for biomedical imaging, which is critical for accurate analysis of tubular structures, representing an incremental advancement over existing methods.
The paper tackles the problem of ambiguous matching in topological segmentation loss functions for tubular structures in biomedical images by introducing a spatial-aware topological loss that leverages spatial domain information, resulting in superior topological accuracy compared to state-of-the-art methods.
Topological correctness is critical for segmentation of tubular structures, which pervade in biomedical images. Existing topological segmentation loss functions are primarily based on the persistent homology of the image. They match the persistent features from the segmentation with the persistent features from the ground truth and minimize the difference between them. However, these methods suffer from an ambiguous matching problem since the matching only relies on the information in the topological space. In this work, we propose an effective and efficient Spatial-Aware Topological Loss Function that further leverages the information in the original spatial domain of the image to assist the matching of persistent features. Extensive experiments on images of various types of tubular structures show that the proposed method has superior performance in improving the topological accuracy of the segmentation compared with state-of-the-art methods. Code is available at https://github.com/JRC-VPLab/SATLoss.