光学相干层析（OCT）成像能实现对视网膜病变可视化检查，是视网膜疾病诊断的重要手段。临床视网膜疾病非常复杂，常同时具有多种病变表征（如：增殖型VI期糖尿病视网膜疾病包含水肿，渗出物，微血管瘤等病变）。但当前基于OCT图像的视网膜疾病自动诊断技术还处于起步阶段，仅对单一病变进行设计，难以诊断复杂疾病。本项目将从定性和定量角度研究OCT图像多病变自动分析方法，实现视网膜疾病的精细化诊断，研究内容包括：（1）提出多病变视网膜OCT图像膜层分割和去噪方法，实现病变区域定位并提升图像质量；（2）提出多标签深度学习病变分类方法，定性辨识多病变类型；（3）研究多病变量化指标自适应估计方法，对病变区域进行多参数定量分析；（4）构建多病变定性、定量指标与视网膜疾病间的映射分类器，精细化定性诊断视网膜疾病。本项研究将系统地构建视网膜OCT图像多病变分析的新理论和方法，有助于提升复杂视网膜疾病的临床诊疗水平。

Optical coherence tomography (OCT) imaging can achieve a visual inspection of the retinal lesions, which is important for the diagnosis of retinal diseases. Clinical retinal disease is very complex, and usually has multiple lesions (e.g., proliferative diabetic retinopathy in the stage VI contains the edema, exudates, microaneurysms, and so on). However, the current OCT image based retinal diseases automatic diagnosis techniques are in the early stages, and only designed for one single lesion, which is hard to diagnose the complex diseases. This project will research on the OCT image multiple lesions automatic analysis method from the qualitative and quantitative perspectives, and achieve the precise diagnosis of the retinal disease. The research content includes the following parts: 1) Propose the layer segmentation and denoising methods for the retinal OCT image with multiple lesions, locating the regions of lesions and improving the quality of the image; 2) Propose a multiple labels deep learning based lesion classification method, determining the types of lesions; 3) Research on the method for the adaptive estimation of lesion’s quantitative indicators, thus analyzing the lesions in a quantitative way; 4) Construct a classifier between the types, quantitative indicators of the lesions and the retinal diseases, precisely diagnosing the retinal diseases. This project will systematically establish new theories and methods for the diagnosis of multiple lesions in OCT image, which is helpful to improve the diagnosis level for the clinical complex retinal diseases.