A semi-supervised fracture-attention model for segmenting tubular objects with improved topological connectivity

Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets. Additionally, current evaluation metrics are insufficient for effectively capturing segmentation fractures.

To address these challenges, we propose a semi-supervised fracture-attention model (SSFA) for tubular object segmentation. SSFA enhances connectivity, reduces fractures, and maintains volumetric accuracy. It outperforms state-of-the-art models in topological performance. Extensive experiments on four public datasets validate the effectiveness of SSFA. Furthermore, we introduce a novel evaluation metric, the fracture rate, which provides an intuitive and quantitative assessment of segmentation fractures.

Our source code is available at http://github.com/Yanfeng-Zhou/SSFA.