SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866911763223543808 |
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| author | Gu, Hanxue Colglazier, Roy Dong, Haoyu Zhang, Jikai Chen, Yaqian Yildiz, Zafer Chen, Yuwen Li, Lin Yang, Jichen Willhite, Jay Meyer, Alex M. Guo, Brian Shah, Yashvi Atul Luo, Emily Rajput, Shipra Kuehn, Sally Bulleit, Clark Wu, Kevin A. Lee, Jisoo Ramirez, Brandon Lu, Darui Levin, Jay M. Mazurowski, Maciej A. |
| author_facet | Gu, Hanxue Colglazier, Roy Dong, Haoyu Zhang, Jikai Chen, Yaqian Yildiz, Zafer Chen, Yuwen Li, Lin Yang, Jichen Willhite, Jay Meyer, Alex M. Guo, Brian Shah, Yashvi Atul Luo, Emily Rajput, Shipra Kuehn, Sally Bulleit, Clark Wu, Kevin A. Lee, Jisoo Ramirez, Brandon Lu, Darui Levin, Jay M. Mazurowski, Maciej A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12974 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI Gu, Hanxue Colglazier, Roy Dong, Haoyu Zhang, Jikai Chen, Yaqian Yildiz, Zafer Chen, Yuwen Li, Lin Yang, Jichen Willhite, Jay Meyer, Alex M. Guo, Brian Shah, Yashvi Atul Luo, Emily Rajput, Shipra Kuehn, Sally Bulleit, Clark Wu, Kevin A. Lee, Jisoo Ramirez, Brandon Lu, Darui Levin, Jay M. Mazurowski, Maciej A. Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods 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. |
| title | SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2401.12974 |