Keywords: Analysis/Processing, Bone
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.
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