Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning

TL;DR

This paper introduces VAFF-Net, a multi-task learning model that adaptively fuses features for joint segmentation, detection, and classification of retinal structures in OCTA images, improving accuracy in complex microvasculature analysis.

Contribution

The paper presents a novel voting-based adaptive feature fusion network with a task-specific voting gate module for improved retinal structure analysis in OCTA images.

Findings

Effective joint segmentation and detection of retinal structures.

Enhanced classification of retinal vascular junctions.

Public release of datasets, code, and evaluation benchmarks.

Abstract

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: features at different spatial positions from a single encoder, and features from multiple encoders. In particular, since the complexity of the microvasculature in OCTA images makes simultaneous precise localization and classification of retinal vascular junctions into bifurcation/crossing a challenging task, we specifically design a task head by combining the heatmap regression and grid classification. We take advantage of three different \textit{en face} angiograms from various retinal layers, rather than following existing methods that use only a single \textit{en face}. To facilitate further research, part of these datasets with the source code and evaluation benchmark have been released for public access:https://github.com/iMED-Lab/VAFF-Net.