Keywords: Other AI/ML, Machine Learning/Artificial Intelligence, One-shot, Shoulder, Foundation Models, Localization, Segmentation
Motivation: Develop automatic labelling capability on anatomical shoulder MRI images with minimal manual annotation.
Goal(s): Leverage large-FOV, low resolution coil sensitivity maps to guide correct positioning of three-plane localizer for shoulder MRI planning.
Approach: Use chained DINO-V2 and SAM foundation models, tuned to MRI localizers and a data driven similarity measure to label shoulder data at scale and transfer to low resolution coil sensitivity maps for CNN model training.
Results: Excellent shoulder region localization with FM on anatomical (91% accuracy) and with CNN model on calibration data (error < 15 mm)
Impact: A data adaptive, chained foundation model-based approach for annotating shoulder regions on MRI anatomical images at scale is shown. This allowed rapid development of model using low-resolution calibration data for correctly positioning three-plane localizer for shoulder anatomical planning and imaging.
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