  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.
