SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT
This work addresses the problem of retinal fluid segmentation in OCT scans for medical imaging applications, but it is incremental as it adapts an existing model to a new domain.
The authors evaluated the Segment Anything Model (SAM) and its adaptations for segmenting retinal fluid in OCT scans, finding it effective but still lagging behind state-of-the-art methods in some cases.
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.