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

• URL: http://arxiv.org/abs/2308.09331v2
• Date: Thu, 31 Aug 2023 07:45:59 GMT
• Title: SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT
• Authors: Botond Fazekas, Jos\'e Morano, Dmitrii Lachinov, Guilherme Aresta, Hrvoje Bogunovi\'c
• Abstract summary: The Segment Anything Model (SAM) has gained significant attention in the field of image segmentation. We conduct a comprehensive evaluation of SAM and its adaptations on a large-scale public dataset of OCTs from RETOUCH challenge. We showcase adapted SAM's efficacy as a powerful segmentation model in retinal OCT scans, although still lagging behind established methods in some circumstances.
• Score: 3.2495192768429924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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