Fractures of the acetabulum, the cavity of the hip that hosts the femoral head, are complex to understand, plan, and surgically reduce. Segmenting bone fragments in CT scans is fundamental for assisting surgeons in their therapeutically process, and can benefit from recent learning-based advances. In this paper, we extended a learning-based network for the semantic segmentation of 6 pelvic bones: left and right hip, left and right femur, sacrum, and lumbar spine. This semantic segmentation is then process by a surgeon to separate fracture fragments, similarly to an existing baseline process. Results on 6 fracture cases show a qualitative improvement of the final fragment segmentation quality. Mostly, the segmentation time is statistically significantly reduced from 94 min to 18 min, in mean, which is a promising step towards using such learning-based method in preoperative clinical routine.