Towards a Generalizable Foundation Model for Multi-Tissue Musculoskeletal MRI Segmentation

Motivation: To evaluate the potential of foundation models for medical imaging analysis. Goal(s): To understand the limitations of foundation models trained for the natural imaging domain, and assess the challenges for translation to complex musculoskeletal anatomy in a rich medical image domain. Approach: A diverse collection of musculoskeletal MRI data was used to assess the generalizability of SAM when applied to a variety of segmentation tasks common to the medical research and clinical setting. Results: SAM performed decently on zero-shot of medical data. The ability of SAM to perform well when finetuned on a spectrum of data, is somewhat lacking and requires additional evaluation. Impact: A foundational model for generalizable musculoskeletal MRI segmentation, such as one fine-tuned on the Segment Anything Model (SAM) has the potential to overcome challenges with generalizability for widespread usage beyond a specific task, reducing burden in medical imaging pipelines.