DynSegNet:Dynamic Architecture Adjustment for Adversarial Learning in Segmenting Hemorrhagic Lesions from Fundus Images

TL;DR

This paper introduces DynSegNet, a novel adaptive neural network architecture that dynamically adjusts its structure during training to improve hemorrhagic lesion segmentation accuracy in fundus images, addressing variability and low contrast issues.

Contribution

The proposed method integrates hierarchical U-shaped encoder-decoder, residual blocks, attention mechanisms, and ASPP modules with dynamic architecture adjustment for enhanced segmentation performance.

Findings

Achieved a Dice coefficient of 0.6802

Attained an IoU of 0.5602

Reached an accuracy of 0.9955

Abstract

The hemorrhagic lesion segmentation plays a critical role in ophthalmic diagnosis, directly influencing early disease detection, treatment planning, and therapeutic efficacy evaluation. However, the task faces significant challenges due to lesion morphological variability, indistinct boundaries, and low contrast with background tissues. To improve diagnostic accuracy and treatment outcomes, developing advanced segmentation techniques remains imperative. This paper proposes an adversarial learning-based dynamic architecture adjustment approach that integrates hierarchical U-shaped encoder-decoder, residual blocks, attention mechanisms, and ASPP modules. By dynamically optimizing feature fusion, our method enhances segmentation performance. Experimental results demonstrate a Dice coefficient of 0.6802, IoU of 0.5602, Recall of 0.766, Precision of 0.6525, and Accuracy of 0.9955, effectively addressing the challenges in fundus image hemorrhage segmentation.[* Corresponding author.]