Automatic segmentation of the knee menisci would facilitate quantitative and morphological evaluation in diseases such as osteoarthritis. We propose a deep convolutional neural network for the segmentation of 3D UTE-Cones Adiabatic T1ρ-weighted volumes of the meniscus. To show the usefulness of the proposed method, we developed the models using regions of interests provided by two radiologists. The method produced strong Dice scores and consistent results with respect to meniscus volume measurement. The inter-observer agreement between the models and the radiologists was very similar to that of the radiologists alone.
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