改进的SegNet结合Grad-CAM，实现OCT眼底图像分层分割的高精度与可解释性。
Enhanced SegNet with Integrated Grad-CAM for Interpretable Retinal Layer Segmentation in OCT Images
- 采用改进池化策略和混合损失函数提升对噪声图像的特征提取能力。
- 在杜克OCT数据集上达到95.77%准确率，Dice系数0.9446，IoU达0.8951。
- 集成Grad-CAM可视化，帮助医生理解模型决策，增强临床可信度。
光学相干断层扫描（OCT）对青光眼、糖尿病视网膜病变和老年黄斑变性等疾病的诊断至关重要。精准的视网膜分层分割可提供关键定量生物标志物，但人工分割耗时且变异大，传统深度学习模型常缺乏可解释性。本文提出一种基于SegNet的改进框架，实现自动化且可解释的视网膜分层分割。通过优化池化策略增强对噪声OCT图像的特征提取能力，采用融合分类交叉熵与Dice损失的混合损失函数，提升对薄层及不平衡结构的分割性能。引入梯度加权类激活映射（Grad-CAM）生成可视化解释，支持临床验证模型决策。在杜克OCT数据集上训练与验证，框架取得95.77%的验证准确率、0.9446的Dice系数和0.8951的交并比（IoU）。各层表现稳健，较薄边界仍具挑战。Grad-CAM可视化聚焦解剖相关区域，与临床生物标志物一致，显著提升透明性。该方法通过架构优化、定制损失与可解释AI的融合，实现了精度与可解释性的平衡，具备标准化OCT分析、提升诊断效率及增强临床对AI工具信任的潜力。
原文摘要 · Abstract (English)
Optical Coherence Tomography (OCT) is essential for diagnosing conditions such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Accurate retinal layer segmentation enables quantitative biomarkers critical for clinical decision-making, but manual segmentation is time-consuming and variable, while conventional deep learning models often lack interpretability. This work proposes an improved SegNet-based deep learning framework for automated and interpretable retinal layer segmentation. Architectural innovations, including modified pooling strategies, enhance feature extraction from noisy OCT images, while a hybrid loss function combining categorical cross-entropy and Dice loss improves performance for thin and imbalanced retinal layers. Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to provide visual explanations, allowing clinical validation of model decisions. Trained and validated on the Duke OCT dataset, the framework achieved 95.77% validation accuracy, a Dice coefficient of 0.9446, and a Jaccard Index (IoU) of 0.8951. Class-wise results confirmed robust performance across most layers, with challenges remaining for thinner boundaries. Grad-CAM visualizations highlighted anatomically relevant regions, aligning segmentation with clinical biomarkers and improving transparency. By combining architectural improvements, a customized hybrid loss, and explainable AI, this study delivers a high-performing SegNet-based framework that bridges the gap between accuracy and interpretability. The approach offers strong potential for standardizing OCT analysis, enhancing diagnostic efficiency, and fostering clinical trust in AI-driven ophthalmic tools.
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