用DeepLabv3+分类型精准分割糖尿病视网膜病变病灶
Advanced Segmentation of Diabetic Retinopathy Lesions Using DeepLabv3+
- 为每类病灶定制二值分割模型，提升针对性
- 在IDRID数据集上实现99%分割准确率
- 适合医学图像分析与眼科AI研究者参考
为提升糖尿病视网膜病变病灶（微动脉瘤、出血、渗出物和软性渗出物）的分割精度，本文针对每类病灶设计了专用的二值分割方法。后处理阶段将各模型输出融合为一张图像，以更清晰分析病灶类型。该方法有效缓解了数据集有限和标注复杂的问题。预处理采用裁剪及LAB空间L通道的对比度受限自适应直方图均衡化；同时引入针对性数据增强策略进一步优化模型性能。基于DeepLabv3+模型，实验在IDRID数据集上达到99%的分割准确率。结果表明，该策略显著提升了医疗图像分析中病灶分割的精确性。
原文摘要 · Abstract (English)
To improve the segmentation of diabetic retinopathy lesions (microaneurysms, hemorrhages, exudates, and soft exudates), we implemented a binary segmentation method specific to each type of lesion. As post-segmentation, we combined the individual model outputs into a single image to better analyze the lesion types. This approach facilitated parameter optimization and improved accuracy, effectively overcoming challenges related to dataset limitations and annotation complexity. Specific preprocessing steps included cropping and applying contrast-limited adaptive histogram equalization to the L channel of the LAB image. Additionally, we employed targeted data augmentation techniques to further refine the model's efficacy. Our methodology utilized the DeepLabv3+ model, achieving a segmentation accuracy of 99%. These findings highlight the efficacy of innovative strategies in advancing medical image analysis, particularly in the precise segmentation of diabetic retinopathy lesions. The IDRID dataset was utilized to validate and demonstrate the robustness of our approach.
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