Towards Cross-Scale Attention and Surface Supervision for Fractured Bone Segmentation in CT

TL;DR

This paper introduces a novel cross-scale attention mechanism and surface supervision strategy to improve fractured bone segmentation in CT scans, addressing challenges posed by fracture variability and bone anatomy.

Contribution

It proposes a new method combining cross-scale attention and surface supervision for more accurate fractured bone segmentation in CT images.

Findings

Achieved 93.36% Dice similarity coefficient on a public dataset.

Reduced average surface distance to 0.85mm.

Improved segmentation performance for fractured bones in CT scans.

Abstract

Bone segmentation is an essential step for the preoperative planning of fracture trauma surgery. The automated segmentation of fractured bone from computed tomography (CT) scans remains challenging, due to the large differences of fractures in position and morphology, and also the inherent anatomical characteristics of different bone structures. To alleviate these issues, we propose a cross-scale attention mechanism as well as a surface supervision strategy for fractured bone segmentation in CT. Specifically, a cross-scale attention mechanism is introduced to effectively aggregate the features among different scales to provide more powerful fracture representation. Moreover, a surface supervision strategy is employed, which explicitly constrains the network to pay more attention to the bone boundary. The efficacy of the proposed method is evaluated on a public dataset containing CT scans with hip fractures. The evaluation metrics are Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance (95HD). The proposed method achieves an average DSC of 93.36%, ASSD of 0.85mm, 95HD of 7.51mm. Our method offers an effective fracture segmentation approach for the pelvic CT examinations, and has the potential to be used for improving the segmentation performance of other types of fractures.