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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords