Unilateral hip joint segmentation with shape priors learned from missing data

The accurate segmentation of the bone from Magnetic Resonance (MR) images of the hip is important for clinical studies and drug trials into conditions like Osteoarthritis. This paper presents an automatic segmentation scheme that utilises a deformable model robust to different field of views by training shape priors from partial and full bone surfaces. The deformable model with these priors were used to segment the hip joint within 16 unilateral 3T MR images having different field of views, so that parts of the model outside the image could be ignored fully without affecting the accuracy of the segmentation within the image. Mean and median Dice's Similarity Coefficients of 0.91 & 0.92 for the femur and 0.86 & 0.88 for one half of the pelvis were obtained using a leave-one-out approach.