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A hybrid segmentation approach for geographic atrophy in fundus auto-fluorescence images for diagnosis of age-related macular degeneration

机译：眼底自发荧光图像中地理萎缩的混合分割方法，用于诊断年龄相关性黄斑变性

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摘要

Fundus auto-fluorescence (FAF) images with hypo-fluorescence indicate geographic atrophy (GA) of the retinal pigment epithelium (RPE) in age-related macular degeneration (AMD). Manual quantification of GA is time consuming and prone to inter- and intra-observer variability. Automatic quantification is important for determining disease progression and facilitating clinical diagnosis of AMD. In this paper we describe a hybrid segmentation method for GA quantification by identifying hypo-fluorescent GA regions from other interfering retinal vessel structures. First, we employ background illumination correction exploiting a non-linear adaptive smoothing operator. Then, we use the level set framework to perform segmentation of hypo-fluorescent areas. Finally, we present an energy function combining morphological scale-space analysis with a geometric model-based approach to perform segmentation refinement of false positive hypo- fluorescent areas due to interfering retinal structures. The clinically apparent areas of hypo-fluorescence were drawn by an expert grader and compared on a pixel by pixel basis to our segmentation results. The mean sensitivity and specificity of the ROC analysis were 0.89 and 0.98%.
机译：具有低荧光的眼底自发荧光（FAF）图像表明与年龄有关的黄斑变性（AMD）中视网膜色素上皮（RPE）的地理萎缩（GA）。手动量化GA非常耗时，而且观察者之间和观察者内部都容易发生变化。自动定量对于确定疾病进展并促进AMD的临床诊断非常重要。在本文中，我们通过识别其他干扰性视网膜血管结构的低荧光GA区域，描述了一种用于GA定量的混合分割方法。首先，我们利用非线性自适应平滑算子来进行背景照明校正。然后，我们使用水平集框架对次荧光区域进行分割。最后，我们提出了一种将形态学尺度空间分析与基于几何模型的方法相结合的能量函数，以对由于干扰视网膜结构而引起的假阳性次荧光区域进行细分。临床上明显的低荧光区域是由专业的分级员绘制的，并在逐个像素的基础上与我们的分割结果进行了比较。 ROC分析的平均敏感性和特异性分别为0.89和0.98％。

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