The Segment Anything Model (SAM) has recently emerged as a significant breakthrough in foundation models, demonstrating remarkable zero-shot performance in object segmentation tasks. While SAM is designed for generalization, it exhibits limitations in handling specific medical imaging tasks that require fine-structure segmentation or precise boundaries. In this paper, we focus on the task of cardiac magnetic resonance imaging (cMRI) short-axis view segmentation using the SAM foundation model. We conduct a comprehensive investigation of the impact of different prompting strategies (including bounding boxes, positive points, negative points, and their combinations) on segmentation performance. We evaluate on two public datasets using the baseline model and models fine-tuned with varying amounts of annotated data, ranging from a limited number of volumes to a fully annotated dataset. Our findings indicate that prompting strategies significantly influence segmentation performance. Combining positive points with either bounding boxes or negative points shows substantial benefits, but little to no benefit when combined simultaneously. We further observe that fine-tuning SAM with a few annotated volumes improves segmentation performance when properly prompted. Specifically, fine-tuning with bounding boxes has a positive impact, while fine-tuning without bounding boxes leads to worse results compared to baseline.

翻译：Segment Anything Model（SAM）近期作为基础模型领域的重大突破而涌现，在物体分割任务中展现出卓越的零样本性能。尽管SAM被设计为通用模型，但在需要精细结构分割或精确边界的特定医学影像任务中，仍存在局限性。本文聚焦于利用SAM基础模型进行心脏磁共振成像（cMRI）短轴视图分割的任务。我们系统研究了不同提示策略（包括边界框、正点、负点及其组合）对分割性能的影响。基于基线模型及使用不同数量标注数据（从有限体积到完全标注数据集）微调的模型，我们在两个公开数据集上进行了评估。研究结果表明，提示策略对分割性能具有显著影响。正点与边界框或负点的组合带来显著优势，但三者同时组合时几乎无额外收益。进一步观察发现，在适当提示条件下，使用少量标注体积微调SAM可提升分割性能。具体而言，带边界框的微调产生积极影响，而无边界框的微调结果较基线更差。