Use of Segment Anything Model (SAM) and MedSAM in the optic disc Segmentation of colour retinal fundus images: Experimental Finding

Abstract

Detection of Optic Disc segmentation in retinal fundus images is important step in identification of various abnormal conditions like diabetic retinopathy, Glaucoma etc. and is an important part of eye that is routinely examined. We have used Segment Anything model (SAM) by Meta AI and based fine-tuned model MedSAM for segmentation of optic disc in retinal fundus images. We have used Indian Diabetic Retinopathy dataset (IDRiD) segmentation part. It consists of 81 original colour fundus images in jpg files split into train and test set. Ground-truth images for the lesions (Microaneurysms, Haemorrhages, Hard Exudates and Soft Exudates divided into train and test set - TIF Files) and Optic Disc (divided into train and test set - TIF Files). This dataset is feed into the deep learning neural network model; Segment Anything Model (SAM) and MedSAM for the image segmentation task. The Dice Similarity Coefficient (DSC) is used in the experiment for observing the model performance. We have used 80% for training and 20% for testing of Indian Diabetic Retinopathy Dataset (IDRiD) in both models. After 100 epochs with 32 batch size it is observed that average Dice Similarity Coefficient (DSC) of SAM and MedSAM models in the optic disc segmentation task are, 85.97 % and 90.15 %. Also, in finding it is observed that SAM and MedSAM both have very low DSC where the fundus images are having very high brightness.