Learning with Explicit Topological Priors for Chest X-Ray Rib Segmentation

Xiaowei Zhao, Chenglong Li, Jin Tang, Chuanfu Li

Abstract: Chest X-ray (CXR) image examination is a primary tool for assessing thoracic abnormalities. It is widely utilized for initial diagnosis and screening of diseases due to its cost-effectiveness and low radiation dose. Segmentation of ribs in CXR images (CXR rib segmentation) facilitates rapid determination of lesion types and locations, thereby alleviating the workload of medical professionals. Deep learning-based methods have achieved significant progress but still face some challenges in CXR rib segmentation, such as the occlusion challenge caused by artifacts and the interlace challenge caused by the spatial overlap of ribs. Therefore, it can be observed that the topological knowledge of ribs is crucial for CXR rib segmentation but neglected in existing methods, including the connectivity and interactivity of ribs. To address these challenges, we propose a novel learning framework that integrates explicit topological priors into segmentation networks for precise CXR rib segmentation. In particular, we introduce two modules including the connectivity prior embedding module and the interactivity prior embedding module. These modules are designed to explicitly encode the continuity and interactivity of ribs into deep learning models for end-to-end training. Both modules are plug-and-play and can be integrated into various networks. We conduct extensive experiments on VinDr-RibCXR and CXRS datasets to evaluate the segmentation accuracy of each rib using multiple metrics. Evaluation and visual results show that our method exhibits strong adaptability, seamlessly integrating with diverse architectures and enhancing performance across various networks. Our code is publicly available at https://github.com/XWei98/LTSeg.

External IDs:dblp:conf/miccai/ZhaoLTL25