Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

TL;DR

This paper presents a deep learning method using transfer learning and a customized loss function for highly accurate and rapid optic disc segmentation from retinal images, demonstrating robustness across diverse datasets.

Contribution

It introduces a novel VGG16-based UNET model with customizations, trained on multiple datasets, achieving state-of-the-art accuracy and efficiency in optic disc segmentation.

Findings

Achieved 99.78% accuracy and 94.73% Dice coefficient.

Segmented optic discs in 0.03 seconds.

Demonstrated robustness across diverse datasets.

Abstract

Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma. In this paper, we propose a deep-learning based approach capable of segmenting the optic disc given a high-precision retinal fundus image. Our approach utilizes a UNET-based model with a VGG16 encoder trained on the ImageNet dataset. This study can be distinguished from other studies in the customization made for the VGG16 model, the diversity of the datasets adopted, the duration of disc segmentation, the loss function utilized, and the number of parameters required to train our model. Our approach was tested on seven publicly available datasets augmented by a dataset from a private clinic that was annotated by two Doctors of Optometry through a web portal built for this purpose. We achieved an accuracy of 99.78\% and a Dice coefficient of 94.73\% for a disc segmentation from a retinal image in 0.03 seconds. The results obtained from comprehensive experiments demonstrate the robustness of our approach to disc segmentation of retinal images obtained from different sources.