Preprint Article Version 1 This version is not peer-reviewed

Segment Anything in OCT: SAM 2 for Volumetric Segmentation of Retinal Biomarkers

Mikhail Kulyabin
* ORCID logo ,
Aleksei Zhdanov
,
Andrey Pershin
,
Gleb Sokolov
,
Anastasia Nikiforova
,
Mikhail Ronkin
ORCID logo ,
Vasilii I Borisov
,
Andreas Maier
ORCID logo
Version 1 : Received: 6 September 2024 / Approved: 9 September 2024 / Online: 9 September 2024 (15:16:17 CEST)

How to cite: Kulyabin, M.; Zhdanov, A.; Pershin, A.; Sokolov, G.; Nikiforova, A.; Ronkin, M.; Borisov, V. I.; Maier, A. Segment Anything in OCT: SAM 2 for Volumetric Segmentation of Retinal Biomarkers. Preprints 2024, 2024090703. https://doi.org/10.20944/preprints202409.0703.v1 Kulyabin, M.; Zhdanov, A.; Pershin, A.; Sokolov, G.; Nikiforova, A.; Ronkin, M.; Borisov, V. I.; Maier, A. Segment Anything in OCT: SAM 2 for Volumetric Segmentation of Retinal Biomarkers. Preprints 2024, 2024090703. https://doi.org/10.20944/preprints202409.0703.v1

Abstract

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology for visualizing retinal layers, aiding in early detection and monitoring of retinal diseases. OCT is useful for detecting diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME), which affect millions of people globally. Over the past decade, the area of application of Artificial Intelligence (AI), particularly Deep Learning (DL), has significantly increased. The number of medical applications is also rising, with solutions from other domains being increasingly applied to OCT. The segmentation of biomarkers is an essential problem that can enhance the quality of retinal disease diagnostics. For 3D OCT scans, AI is beneficial since manual segmentation is very labor-intensive. In this paper, we employ the new SAM 2 and MedSAM 2 for the segmentation of OCT volumes for two open-source datasets, comparing their performance with the traditional U-Net. The model achieved an overall Dice score of 0.913 and 0.902 for macular holes (MH) and intraretinal cysts (IRC) on OIMHS and 0.888 and 0.909 for intraretinal fluid (IRF) and pigment epithelial detachment (PED) on the AROI dataset, respectively.

Keywords

OCT; segmentation; SAM; MedSAM; AMD; DME; Retina

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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