Surpassing state of the art on AMD area estimation from RGB fundus images through careful selection of U-Net architectures and loss functions for class imbalance

TL;DR

This paper presents a refined U-Net based framework for AMD lesion segmentation in RGB fundus images, achieving state-of-the-art results by optimizing architecture and loss functions to handle class imbalance.

Contribution

It introduces a novel configuration of U-Net architectures and training strategies that surpass previous methods on the ADAM AMD detection benchmark.

Findings

Outperforms all prior ADAM challenge submissions in multi-class segmentation

Effective use of specialized loss functions for class imbalance

Enhanced segmentation accuracy through architecture and preprocessing improvements

Abstract

Age-related macular degeneration (AMD) is one of the leading causes of irreversible vision impairment in people over the age of 60. This research focuses on semantic segmentation for AMD lesion detection in RGB fundus images, a non-invasive and cost-effective imaging technique. The results of the ADAM challenge - the most comprehensive AMD detection from RGB fundus images research competition and open dataset to date - serve as a benchmark for our evaluation. Taking the U-Net connectivity as a base of our framework, we evaluate and compare several approaches to improve the segmentation model's architecture and training pipeline, including pre-processing techniques, encoder (backbone) deep network types of varying complexity, and specialized loss functions to mitigate class imbalances on image and pixel levels. The main outcome of this research is the final configuration of the AMD detection framework, which outperforms all the prior ADAM challenge submissions on the multi-class segmentation of different AMD lesion types in non-invasive RGB fundus images. The source code used to conduct the experiments presented in this paper is made freely available.