Segmentation of the femur and pelvis from 3D data is prerequisite of patient specific planning and simulation for hip surgery. Separation of the femoral head and acetabulum is one of main difficulties in the diseased hip joint due to deformed shapes and extreme narrowness of the joint space. In this paper, we develop a hierarchical multi-object statistical shape model representing joint structure for automated segmentation of the diseased hip from 3D CT images. In order to represent shape variations as well as pose variations of the femur against the pelvis, both shape and pose variations are embedded in a combined pelvis and femur statistical shape model (SSM). Further, the whole combined SSM is divided into individual pelvis and femur SSMs and a partial combined SSM only including the acetabulum and proximal femur. The partial combined SSM maintains the consistency of the two bones by imposing the constraint that the shapes of the overlapped portions of the individual and partial combined SSMs are identical. The experimental results show that segmentation and separation accuracy of the femur and pelvis was improved using the proposed method compared with independent use of the pelvis and femur SSMs.
We demonstrated that the proposed hierarchical multi-atlas method improved the accuracy of muscle segmentation from CT images, in which large inter-patient variability and insufficient contrast were involved.
Quantitative analysis of the results shows that the method performed better than two other recent methods in the literature. Experimental results demonstrate that the proposed framework is effective and can be used in computer-guided dental implant surgery.
The new semiautomatic method segments acetabular cartilage by fully utilizing the statistical and anatomical information in CT arthrography datasets. This method for hip joint cartilage segmentation has potential for use in many clinical applications.
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