This paper presents FeTal-SAM, a novel adaptation of the Segment Anything Model (SAM) tailored for fetal brain MRI segmentation. Traditional deep learning methods often require large annotated datasets for a fixed set of labels, making them inflexible when clinical or research needs change. By integrating atlas-based prompts and foundation-model principles, FeTal-SAM addresses two key limitations in fetal brain MRI segmentation: (1) the need to retrain models for varying label definitions, and (2) the lack of insight into whether segmentations are driven by genuine image contrast or by learned spatial priors. We leverage multi-atlas registration to generate spatially aligned label templates that serve as dense prompts, alongside a bounding-box prompt, for SAM's segmentation decoder. This strategy enables binary segmentation on a per-structure basis, which is subsequently fused to reconstruct the full 3D segmentation volumes. Evaluations on two datasets, the dHCP dataset and an in-house dataset demonstrate FeTal-SAM's robust performance across gestational ages. Notably, it achieves Dice scores comparable to state-of-the-art baselines which were trained for each dataset and label definition for well-contrasted structures like cortical plate and cerebellum, while maintaining the flexibility to segment any user-specified anatomy. Although slightly lower accuracy is observed for subtle, low-contrast structures (e.g., hippocampus, amygdala), our results highlight FeTal-SAM's potential to serve as a general-purpose segmentation model without exhaustive retraining. This method thus constitutes a promising step toward clinically adaptable fetal brain MRI analysis tools.

翻译：本文提出FeTal-SAM，一种针对胎儿脑部MRI分割任务进行定制化改进的通用分割模型（SAM）。传统深度学习方法通常需要针对固定标签集的大规模标注数据集进行训练，当临床或研究需求发生变化时缺乏灵活性。通过整合基于图谱的提示与基础模型原理，FeTal-SAM解决了胎儿脑部MRI分割中的两个关键局限：（1）针对不同标签定义需重新训练模型的需求；（2）难以判断分割结果是由真实图像对比度驱动还是由学习到的空间先验驱动。我们利用多图谱配准技术生成空间对齐的标签模板，将其作为密集提示，并结合边界框提示，共同输入SAM的分割解码器。该策略支持按脑结构进行二值分割，随后通过融合重建完整的三维分割体积。在dHCP数据集和内部数据集上的评估表明，FeTal-SAM在不同孕周数据中均表现出鲁棒性能。值得注意的是，对于皮质板和小脑等高对比度结构，其Dice分数与针对各数据集和标签定义专门训练的最先进基线模型相当，同时保持了对用户指定解剖结构进行分割的灵活性。尽管对于海马体、杏仁核等对比度微弱的精细结构，其精度略有下降，但实验结果凸显了FeTal-SAM无需大量重复训练即可作为通用分割模型的潜力。因此，该方法为开发具有临床适应性的胎儿脑部MRI分析工具迈出了重要一步。