Keywords: Segmentation, Cancer
Manual segmentation of normal and tumor tissues on MRI is a traditional approach that is still used, but it is a very challenging and time-consuming method requiring a high level of precision and has shown inter-reader contouring variability. Therefore, semi- or fully automated segmentation algorithms are essential to segment tumors such as neck nodal metastases. The present study aimed to apply the previously developed deep learning-based self-distilled masked image transformer method for auto-segmenting neck nodal metastases on longitudinal T2-weighted MR images.
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