Automated CT Segmentation of Diseased Hip Using Hierarchical and Conditional Statistical Shape Models

Futoshi Yokota¹, Toshiyuki Okada², Masaki Takao², Nobuhiko Sugano², Yukio Tada³, Noriyuki Tomiyama², and Yoshinobu Sato²

¹Graduate School of Engineering, Kobe University, Japan

²Graduate School of Medicine, Osaka University, Japan
yoshi@image.med.osaka-u.ac.jp

³Graduate School of System Informatics, Kobe University, Japan

Abstract. Segmentation of the femur and pelvis is a prerequisite for patient-specific planning and simulation for hip surgery. Accurate boundary determination of the femoral head and acetabulum is the primary challenge in diseased hip joints because of deformed shapes and extreme narrowness of the joint space. To overcome this difficulty, we investigated a multi-stage method in which the hierarchical hip statistical shape model (SSM) is initially utilized to complete segmentation of the pelvis and distal femur, and then the conditional femoral head SSM is used under the condition that the regions segmented during the previous stage are known. CT data from 100 diseased patients categorized on the basis of their disease type and severity, which included 200 hemi-hips, were used to validate the method, which delivered significantly increased segmentation accuracy for the femoral head.

LNCS 8150, p. 190 ff.

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