A 3D multi-task network for the automatic segmentation of CT images featuring hip osteoarthritis.

Total hip arthroplasty (THA) is the primary treatment for end-stage hip osteoarthritis, with successful outcomes depending on precise preoperative planning that requires accurate segmentation and reconstruction of periarticular bone of the hip joint. However, patients with hip osteoarthritis typically exhibit pathological characteristics, including joint space narrowing, femoroacetabular impingement, osteophyte formation. These changes present significant challenges for traditional manual or semi-automatic segmentation methods. To address these challenges, this study proposed a novel 3D UNet-based multi-task network to achieve rapid and accurate segmentation and reconstruction of the periarticular bone in hip osteoarthritis patients. The bone segmentation main network incorporated the Transformer module during the encoder to effectively capture spatial anatomical features, while a boundary-optimization branch was designed to address segmentation challenges at the acetabular-femoral interface. These branches were jointly optimized through a multi-task loss function, with an oversampling strategy introduced to enhance the network's feature learning capability for complex structures. The experimental results showed that the proposed method achieved excellent performance on the test set with hip osteoarthritis. The average Dice coefficient was 0.945 (0.96 for femur, 0.93 for hip), with an overall precision of 0.95 and recall of 0.97. In terms of the boundary matching metrics, the average surface distance (ASD) and the 95% Hausdorff distance (HD95) were 0.58 mm and 3.55 mm, respectively. The metrics showed that the proposed automatic segmentation network achieved high accuracy in segmenting the periarticular bone of the hip joint, generating reliable 2D masks and 3D models, thereby demonstrating significant potential for supporting THA surgical planning.