动态调整网络结构，提升眼底出血病灶分割精度
DynSegNet:Dynamic Architecture Adjustment for Adversarial Learning in Segmenting Hemorrhagic Lesions from Fundus Images
- 基于对抗学习动态优化网络结构，融合注意力与多尺度特征
- 在眼底图像上实现0.68的Dice系数和0.56的IoU
- 适合眼科医学图像分割研究者参考
出血性病灶分割在眼科诊断中至关重要，直接影响早期疾病发现、治疗方案制定及疗效评估。然而，由于病灶形态多样、边界模糊且与背景组织对比度低，该任务面临巨大挑战。为提升诊断准确率和治疗效果，本文提出一种基于对抗学习的动态架构调整方法，结合分层U型编码器-解码器、残差块、注意力机制与ASPP模块，通过动态优化特征融合提升分割性能。实验结果表明，该方法在眼底图像出血分割任务中取得0.6802的Dice系数、0.5602的IoU、0.766的召回率、0.6525的精确率和0.9955的准确率，有效应对了相关挑战。
原文摘要 · Abstract (English)
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.]
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