The Segment Anything Model (SAM) has gained significant attention in the field of image segmentation due to its impressive capabilities and prompt-based interface. While SAM has already been extensively evaluated in various domains, its adaptation to retinal OCT scans remains unexplored. To bridge this research gap, we conduct a comprehensive evaluation of SAM and its adaptations on a large-scale public dataset of OCTs from RETOUCH challenge. Our evaluation covers diverse retinal diseases, fluid compartments, and device vendors, comparing SAM against state-of-the-art retinal fluid segmentation methods. Through our analysis, we showcase adapted SAM's efficacy as a powerful segmentation model in retinal OCT scans, although still lagging behind established methods in some circumstances. The findings highlight SAM's adaptability and robustness, showcasing its utility as a valuable tool in retinal OCT image analysis and paving the way for further advancements in this domain.

翻译：Segment Anything Model（SAM）凭借其卓越能力与基于提示（prompt-based）的交互界面，在图像分割领域受到广泛关注。尽管SAM已在多个领域得到充分评估，但其在视网膜OCT扫描中的适配性尚属空白。为填补这一研究缺口，我们在RETOUCH挑战赛提供的大规模公开OCT数据集上，对SAM及其适配模型进行了全面评估。评估涵盖多种视网膜疾病、积液腔室及设备厂商，并将SAM与当前最优的视网膜积液分割方法进行对比。通过分析，我们展示了适配SAM作为视网膜OCT扫描中的高效分割模型的有效性，尽管在某些场景下其性能仍落后于现有方法。研究结果凸显了SAM的适应性与鲁棒性，证明其可作为视网膜OCT图像分析中的宝贵工具，并为该领域的进一步发展奠定基础。