Fully Automatic CT Segmentation for Computer-Assisted Pre-operative Planning of Hip Arthroscopy

Chu, Chengwen; Chen, Cheng; Zheng, Guoyan (2014). Fully Automatic CT Segmentation for Computer-Assisted Pre-operative Planning of Hip Arthroscopy. In: Luo, Xiongbiao; Reichl, Tobias; Mirota, Daniel; Soper, Timothy (eds.) CARE 2014, LNCS 8899. Lecture Notes in Computer Science: Vol. 8899 (pp. 55-63). Cham: Springer 10.1007/978-3-319-13410-9_6

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Extraction of both pelvic and femoral surface models of a hip joint from CT data for computer-assisted pre-operative planning of hip arthroscopy is addressed. We present a method for a fully automatic image segmentation of a hip joint. Our method works by combining fast random forest (RF) regression based landmark detection, atlas-based segmentation, with articulated statistical shape model (aSSM) based hip joint reconstruction. The two fundamental contributions of our method are: (1) An improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the atlas-based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Validation on 30 hip CT images show that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur surfaces with an average accuracy of 0.59 mm, 0.62 mm, and 0.58 mm, respectively.

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