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Abstract #0840

Automated Deep Learning based 3D Hip Segmentation in PD-weighted MR images of a large-scale cohort study

Marc Fischer1,2, Sven Walter1, Christian Klinger1, Thomas Küstner1,2,3, Bin Yang2, Mike Notohamiprodjo1, and Fritz Schick1

1Department of Radiology, University Hospital Tübingen, Tübingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 3School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

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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