Segmentation of lesions within the liver is pivotal for surveillance, diagnosis and treatment planning, and automated or semi-automated approaches can aid clinical workflows. Many patients that are under surveillance may have had previous surgery, meaning their scans will contain post-surgical features. We investigate the use of a deep learning segmentation model for non-contrast MRI that can distinguish between lesions, surgical clips and resection-induced fluid-filling regions. We report mean dice scores of 0.55, 0.72 and 0.88 for these classes respectively, demonstrating the potential of this model for semi-automated workflows.
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