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.

翻译：精确的磁共振成像（MRI）分割对于临床决策至关重要，但手动分割仍然费时费力。基于卷积神经网络（CNN）的方法虽然能够实现准确高效的分割，但通常对MRI的对比度变化、强度不均匀性和序列差异的泛化能力较差。尽管基于Transformer的通用分割模型（SAM）在自然图像中展现出卓越的泛化性能，但现有适配方法往往仅将MRI视为另一种成像模态，忽略了其特有的挑战。本文提出SAMRI——一个专门针对MRI的SAM模型，该模型在涵盖全身器官与病变的110万张标注MR切片上完成训练与验证。我们证明，通过采用两阶段策略微调SAM的掩码解码器，可使其有效适配MRI任务：与全模型重训练相比，训练时间减少94%，可训练参数量降低96%。在多样化的MRI分割任务中，SAMRI的平均Dice系数达到0.87，在各类解剖区域均实现了最先进的精度，并对未见结构（尤其是临床关键的小型结构）表现出强大的泛化能力。此外，我们提供了完整的训练到推理流程及用户友好的本地图形界面，支持在标准设备上交互式应用预训练的SAMRI模型，为实际场景中的MRI分割部署提供了便利。