Protocol adaptive Stacked transfer learning (STL) U-NET with small dataset training for soft tissue segmentation in dynamic speech MRI
Subin Erattakulangara, Karthika Kelat, Sajan Goud Lingala · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2023
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.