  Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering
non-invasive and high-quality insights into the human body. Precise
segmentation of MRIs into different organs and tissues would be highly
beneficial since it would allow for a higher level of understanding of the
image content and enable important 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 for use, 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 across 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 over 300 annotated volumes
and 8485 annotated slices across diverse anatomic regions; (2) investigating
several standard network architectures and strategies for automated
segmentation; (3) introducing SegmentAnyBone, an innovative foundational
model-based approach that extends 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 an external dataset. We publicly release our model at
https://github.com/mazurowski-lab/SegmentAnyBone.
