The generalization capability of deep learning-based segmentation algorithms across different sites and vendors, as well as MRI data with high variance in contrast, is limited. This affects the usability of such automated segmentation algorithms in clinical settings. The lack of freely accessible medical datasets additionally limits the development of stable models. In this work, we explore the benefits of adding a simulated dataset, containing realistic contrast variance, into the training procedure of the neural network for one of the most clinically important segmentation tasks, the CMR ventricular cavity segmentation.
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