SAMRI: Segment Anything Model for MRI

• URL: http://arxiv.org/abs/2510.26635v1
• Date: Thu, 30 Oct 2025 16:04:00 GMT
• Title: SAMRI: Segment Anything Model for MRI
• Authors: Zhao Wang, Wei Dai, Thuy Thanh Dao, Steffen Bollmann, Hongfu Sun, Craig Engstrom, Shekhar S. Chandra,
• Abstract summary: Convolutional neural network (CNN)-based methods can be accurate and efficient, but often generalize poorly to MRI's variable contrast, intensity inhomogeneity, and protocols.<n>We present SAMRI, an MRI-specialized SAM trained and validated on 1.1 million labeled MR slices spanning whole-body organs and pathologies.
• Score: 7.206770018823158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate magnetic resonance imaging (MRI) segmentation is crucial for clinical decision-making, but remains labor-intensive when performed manually. Convolutional neural network (CNN)-based methods can be accurate and efficient, but often generalize poorly to MRI's variable contrast, intensity inhomogeneity, and protocols. Although the transformer-based Segment Anything Model (SAM) has demonstrated remarkable generalizability in natural images, existing adaptations often treat MRI as another imaging modality, overlooking these modality-specific challenges. We present SAMRI, an MRI-specialized SAM trained and validated on 1.1 million labeled MR slices spanning whole-body organs and pathologies. We demonstrate that SAM can be effectively adapted to MRI by simply fine-tuning its mask decoder using a two-stage strategy, reducing training time by 94% and trainable parameters by 96% versus full-model retraining. Across diverse MRI segmentation tasks, SAMRI achieves a mean Dice of 0.87, delivering state-of-the-art accuracy across anatomical regions and robust generalization on unseen structures, particularly small and clinically important structures.

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