Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of the MRIs into different organs and tissues would be very beneficial as it would allow more accurate measurements, which are essential for accurate diagnosis and effective treatment planning. Specifically, segmenting bones in MRI would allow for more quantitative assessments of musculoskeletal conditions, while such assessments are largely absent in current radiological practice. The difficulty of bone MRI segmentation is illustrated by the fact that limited algorithms are publicly available, and those contained in the literature typically address a specific anatomic area. In our study, we propose a versatile, publicly available deep learning model for bone segmentation in MRI at multiple standard MRI locations. The proposed model can operate in two modes: fully automated segmentation and prompt-based segmentation. Our contributions include (1) collecting and annotating a new MRI dataset across various MRI protocols, encompassing 320 annotated volumes and more than 10k annotated slices across diverse anatomic regions
(2) investigating several standard network architectures and strategies for automated segmentation
(3) introducing SegmentAnyBone, an innovative foundation model-based approach that extends the Segment Anything Model (SAM)
(4) comparative analysis of our algorithm and previous approaches
and (5) generalization analysis of our algorithm across different anatomical locations and MRI sequences, as well as three external datasets. We publicly release our model at Github Code.