We develop a protocol adaptive Stacked transfer learning (STL) U-NET for soft tissue segmentation in dynamic speech MRI. Our approach leverages knowledge from large open-source datasets, and only needs to be trained on small number of protocol specific images (of the order of 20 images). We demonstrate the utility of STL U-NET in efficiently segmenting soft-tissue articulators from three different protocols with different field strengths, vendors, acquisition, reconstruction. Using the DICE similarity metric, we demonstrate segmentation accuracies with our approach to be at the level of manual 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