Fully Automatic Segmentation of Hip CT Images

Chu, Chengwen; Bai, Junjie; Wu, Xiaodong; Zheng, Guoyan (2016). Fully Automatic Segmentation of Hip CT Images. In: Zheng, Guoyan; Li, Shuo (eds.) Computational Radiology for Orthopaedic Interventions. Lecture Notes in Computational Vision and Biomechanics: Vol. 23 (pp. 91-110). Cham: Springer 10.1007/978-3-319-23482-3_5

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Automatic segmentation of the hip joint with pelvis and proximal femur surfaces from CT images is essential for orthopedic diagnosis and surgery. It remains challenging due to the narrowness of hip joint space, where the adjacent surfaces of acetabulum and femoral head are hardly distinguished from each other. This chapter presents a fully automatic method to segment pelvic and proximal femoral surfaces from hip CT images. A coarse-to-fine strategy was proposed to combine multi-atlas segmentation with graph-based surface detection. The multi-atlas segmentation step seeks to coarsely extract the entire hip joint region. It uses automatically detected anatomical landmarks to initialize and select the atlas and accelerate the segmentation. The graph based surface detection is to refine the coarsely segmented hip joint region. It aims at completely and efficiently separate the adjacent surfaces of the acetabulum and the femoral head while preserving the hip joint structure. The proposed strategy was evaluated on 30 hip CT images and provided an average accuracy of 0.55, 0.54, and 0.50 mm for segmenting the pelvis, the left and right proximal femurs, respectively.

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