  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.
