Analysis of geometrical and structural properties of the hip is of great importance to allow for meaningful comparison of significant findings. Especially with regard to large cohort studies manual processing of large 3D volumes becomes infeasible and thus automated processing is required. In this work, a Deep Learning driven algorithm is proposed which performs automated hip segmentation of 3D MRI datasets, requiring few training data and being able to perform accurate semantic bone segmentation in spite of complex anatomical structures sharing similar tissue characteristics.
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