Keywords: Analysis/Processing, MR Fingerprinting/Synthetic MR, Age-Agnostic, Pan-Contrast, Cross-Vendor, Cross-Site, Anomaly, Variable-Resolution, Whole-Brain
Motivation: Current MRI segmentation methods are limited to certain scan types or do not consider MR physics, which negatively affects segmentation quality in some cases.
Goal(s): Learn and validate pan-contrast MRI segmentation.
Approach: We introduce a framework that accurately reflects tissue properties and MR physics in generating images for all MR sequences using randomized scanning and noise parameters, to aid learning and validation of pan-contrast segmentation.
Results: UltBrainNet outperforms the state-of-the-art in 87% of labels for conventional MR images. UBN generalizes across the human lifespan, including challenging neonate cases, and outperforms the SOTA in 100% of labels for low-resolution, variable-orientation, and pathology cases.
Impact: UBN offers a comprehensive solution for consistent segmentation across all MR image contrasts, vendors, resolutions, sites, preprocessing methods, and age groups.
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