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The Segment Anything Model (SAM) has gained significant attention in thefield of image segmentation due to its impressive capabilities and prompt-basedinterface. While SAM has already been extensively evaluated in various domains,its adaptation to retinal OCT scans remains unexplored. To bridge this researchgap, we conduct a comprehensive evaluation of SAM and its adaptations on alarge-scale public dataset of OCTs from RETOUCH challenge. Our evaluationcovers 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 powerfulsegmentation model in retinal OCT scans, although still lagging behindestablished methods in some circumstances. The findings highlight SAM'sadaptability and robustness, showcasing its utility as a valuable tool inretinal OCT image analysis and paving the way for further advancements in thisdomain.

SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT