Automatic Diabetic Retinopathy Lesion Segmentation in UW-OCTA Images Using Transfer Learning

Abstract

Regular retinal screening and timely treatment are the only ways to avoid vision loss due to Diabetic Retinopathy (DR). However, the insufficiency of ophthalmologists or optometrists makes DR screening and treatment programs challenging for the growing global diabetic population. Computer-aided automatic DR screening and detection systems will be a more sustainable approach to deal with this situation. The Diabetic Retinopathy Analysis Challenge 2022 (DRAC22) in association with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention 2022 (MICCAI 2022) created the opportunity for researchers worldwide to work on automatic DR diagnosis procedure on UW-OCTA images of the retina. As automatic segmentation of different DR lesions is the first and most crucial step in the DR screening procedure, we addressed the task of “Segmentation of Diabetic Retinopathy Lesions” among three different tasks of the challenge. We used the transfer learning technique to automatically segment lesions from the retinal images. The chosen pre-trained deep learning model is trained, validated, and tested on the DRAC22 segmentation dataset. It showed a mean Dice Similarity Coefficient (DSC) of 32.06% and a mean Intersection over Union (IoU) of 22.05% on the test dataset during the challenge submission. Some variations in the training procedure lead the model’s performance to a mean DSC of 43.36 % and a mean IoU of 31.03% on the test dataset during post-challenge submission. The link to the repository of code is: https://github:com/Sufianlab/FS_AS_DRAC22