# SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI arXiv: https://arxiv.org/abs/2401.12974 ยท HTML: https://mlsanity.com/paper/2401.12974/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 48 references that are in the map. - First posted on arXiv 23 Jan 2024. ## Builds on (most cited first, 20 of 20) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021) - [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016) - [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018) - [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021) - [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022) - [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023) - [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022) - [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023) - [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023) - [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017) - [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020) - [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018) - [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023) - [NiftyNet: a deep-learning platform for medical imaging](https://mlsanity.com/paper/1709.03485/) (2017) - [Deep learning in radiology: an overview of the concepts and a survey of the state of the art](https://mlsanity.com/paper/1802.08717/) (2018) - [Lumbar spine segmentation in MR images: a dataset and a public benchmark](https://mlsanity.com/paper/2306.12217/) (2023) - [Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans](https://mlsanity.com/paper/2302.00162/) (2023) ## Built on by (most cited first, 14 of 14) - [Segment anything model 2: an application to 2D and 3D medical images](https://mlsanity.com/paper/2408.00756/) (2024) - [MRI-CORE: A Foundation Model for Magnetic Resonance Imaging](https://mlsanity.com/paper/2506.12186/) (2025) - [MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI](https://mlsanity.com/paper/2405.05944/) (2024) - [Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis](https://mlsanity.com/paper/2502.09779/) (2025) - [Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2](https://mlsanity.com/paper/2505.01854/) (2025) - [SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images](https://mlsanity.com/paper/2408.15224/) (2024) - [OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis](https://mlsanity.com/paper/2602.04547/) (2026) - [Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition](https://mlsanity.com/paper/2404.09683/) (2024) - [Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan](https://mlsanity.com/paper/2504.04066/) (2025) - [BreastSegNet: Multi-label Segmentation of Breast MRI](https://mlsanity.com/paper/2507.13604/) (2025) - [Towards MR-Based Trochleoplasty Planning](https://mlsanity.com/paper/2508.06076/) (2025) - [Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation](https://mlsanity.com/paper/2607.07219/) (2026) - [LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification](https://mlsanity.com/paper/2605.30829/) (2026) - [MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation](https://mlsanity.com/paper/2608.00195/) (2026) ## Abstract (arXiv) 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.