SAMRI: Segment Anything Model for MRI

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 variable contrast, intensity inhomogeneity, and sequences. 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 fine-tuning its mask decoder using a two-stage strategy, reducing training time by 94 percent and trainable parameters by 96 percent compared to 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 clinically important structures. In addition, we provide a complete training-to-inference pipeline and a user-friendly local graphical interface that enables interactive application of pretrained SAMRI models on standard machines, facilitating practical deployment for real-world MRI segmentation.