Doheny Eye Institute, University of Southern California, Los Angeles, California.
Invest Ophthalmol Vis Sci. 2013 Dec 30;54(13):8375-83. doi: 10.1167/iovs.13-12552.
Geographic atrophy (GA) is the atrophic late-stage manifestation of age-related macular degeneration (AMD), which may result in severe vision loss and blindness. The purpose of this study was to develop a reliable, effective approach for GA segmentation in both spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) images using a level set-based approach and to compare the segmentation performance in the two modalities.
To identify GA regions in SD-OCT images, three retinal surfaces were first segmented in volumetric SD-OCT images using a double-surface graph search scheme. A two-dimensional (2-D) partial OCT projection image was created from the segmented choroid layer. A level set approach was applied to segment the GA in the partial OCT projection image. In addition, the algorithm was applied to FAF images for the GA segmentation. Twenty randomly chosen macular SD-OCT (Zeiss Cirrus) volumes and 20 corresponding FAF (Heidelberg Spectralis) images were obtained from 20 subjects with GA. The algorithm-defined GA region was compared with consensus manual delineation performed by certified graders.
The mean Dice similarity coefficients (DSC) between the algorithm- and manually defined GA regions were 0.87 ± 0.09 in partial OCT projection images and 0.89 ± 0.07 in registered FAF images. The area correlations between them were 0.93 (P < 0.001) in partial OCT projection images and 0.99 (P < 0.001) in FAF images. The mean DSC between the algorithm-defined GA regions in the partial OCT projection and registered FAF images was 0.79 ± 0.12, and the area correlation was 0.96 (P < 0.001).
A level set approach was developed to segment GA regions in both SD-OCT and FAF images. This approach demonstrated good agreement between the algorithm- and manually defined GA regions within each single modality. The GA segmentation in FAF images performed better than in partial OCT projection images. Across the two modalities, the GA segmentation presented reasonable agreement.
地理萎缩（GA）是年龄相关性黄斑变性（AMD）的萎缩晚期表现，可能导致严重的视力丧失和失明。本研究的目的是开发一种基于水平集的方法，用于在光谱域光学相干断层扫描（SD-OCT）和眼底自发荧光（FAF）图像中可靠有效地分割 GA，并比较两种模态的分割性能。
为了在 SD-OCT 图像中识别 GA 区域，首先使用双表面图搜索方案在容积 SD-OCT 图像中分割三个视网膜表面。从分割的脉络膜层创建二维（2-D）部分 OCT 投影图像。应用水平集方法分割部分 OCT 投影图像中的 GA。此外，该算法还应用于 FAF 图像进行 GA 分割。从 20 名患有 GA 的患者中获得 20 个随机选择的黄斑 SD-OCT（蔡司 Cirrus）体积和 20 个相应的 FAF（海德堡 Spectralis）图像。将算法定义的 GA 区域与经过认证的分级员进行的共识手动描绘进行比较。
在部分 OCT 投影图像中，算法和手动定义的 GA 区域之间的平均骰子相似系数（DSC）为 0.87 ± 0.09，在注册的 FAF 图像中为 0.89 ± 0.07。它们之间的面积相关性分别为 0.93（P < 0.001）在部分 OCT 投影图像和 0.99（P < 0.001）在 FAF 图像中。在部分 OCT 投影和注册的 FAF 图像中，算法定义的 GA 区域之间的平均 DSC 为 0.79 ± 0.12，面积相关性为 0.96（P < 0.001）。
开发了一种水平集方法来分割 SD-OCT 和 FAF 图像中的 GA 区域。该方法在每种单一模态中均显示出算法和手动定义的 GA 区域之间的良好一致性。FAF 图像中的 GA 分割优于部分 OCT 投影图像。在两种模态中，GA 分割具有合理的一致性。
