Towards a learning-based CT segmentation of acetabular fractures

Andy Zhang; Mehdi Boudissa; Maxime Nemo; Jérôme Tonetti; Matthieu Chabanas

doi:10.1117/12.2655788

3 April 2023 Towards a learning-based CT segmentation of acetabular fractures

Andy Zhang, Mehdi Boudissa, Maxime Nemo, Jérôme Tonetti, Matthieu Chabanas

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 124662E (2023) https://doi.org/10.1117/12.2655788
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

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 six 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.

Citation Download Citation

Andy Zhang, Mehdi Boudissa, Maxime Nemo, Jérôme Tonetti, and Matthieu Chabanas "Towards a learning-based CT segmentation of acetabular fractures", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 124662E (3 April 2023); https://doi.org/10.1117/12.2655788

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