Abstract
Hip Osteoarthritis (OA) is a common pathological condition among the elderly population, which is mainly characterized by cartilage degeneration. Accurate segmentation of the cartilage tissue over MRIs facilitates quantitative investigations into the disease progression. We propose an automated approach to segment the hip joint cartilage as a single unit from routine clinical MRIs utilizing a voxel-based classification approach. We extracted a rich feature set from the MRIs, which consisting of normalized image intensity-based, local image structure-based, and geometry-based features. We have evaluated the proposed method using routine clinical hip MR images taken from asymptomatic elderly and diagnosed OA patients. MR images from both cohorts show full or partial loss of thickness due to aging or hip OA progression. The proposed algorithm shows good accuracy compared to the manual segmentations with a mean DSC value of 0.74, even with a high prevalence of cartilage defects in the MRI dataset.
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Harischandra, N., Dharmaratne, A., Cicuttini, F.M., Wang, Y. (2020). Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_69
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