Ishikawa Hiroshi, Kim Jongsick, Friberg Thomas R, Wollstein Gadi, Kagemann Larry, Gabriele Michelle L, Townsend Kelly A, Sung Kyung R, Duker Jay S, Fujimoto James G, Schuman Joel S
Department of Ophthalmology, UPMC Eye Center, Eye and Ear Institute, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA.
Invest Ophthalmol Vis Sci. 2009 Mar;50(3):1344-9. doi: 10.1167/iovs.08-2703. Epub 2008 Oct 24.
To develop a semiautomated method to visualize structures of interest (SoIs) along their contour within three-dimensional, spectral domain optical coherence tomography (3D SD-OCT) data, without the need for segmentation.
With the use of two SD-OCT devices, the authors obtained 3D SD-OCT data within 6 x 6 x 1.4-mm and 6 x 6 x 2-mm volumes, respectively, centered on the fovea in healthy eyes and in eyes with retinal pathology. C-mode images were generated by sampling a variable thickness plane semiautomatically modeled to fit the contour of the SoI. Unlike published and commercialized methods, this method did not require retinal layer segmentation, which is known to fail frequently in the presence of retinal pathology. Four SoIs were visualized for healthy eyes: striation of retinal nerve fiber (RNF), retinal capillary network (RCN), choroidal capillary network (CCN), and major choroidal vasculature (CV). Various SoIs were visualized for eyes with retinal pathology.
Seven healthy eyes and seven eyes with retinal pathology (cystoid macular edema, central serous retinopathy, vitreoretinal traction, and age-related macular degeneration) were imaged. CCN and CV were successfully visualized in all eyes, whereas RNF and RCN were visualized in all healthy eyes and in 42.8% of eyes with pathologies. Various SoIs were successfully visualized in all eyes with retinal pathology.
The proposed C-mode contour modeling may provide clinically useful images of SoIs even in eyes with severe pathologic changes in which segmentation algorithms fail.
开发一种半自动方法，用于在三维光谱域光学相干断层扫描（3D SD-OCT）数据中沿感兴趣结构（SoI）的轮廓可视化这些结构，而无需进行分割。
作者使用两台SD-OCT设备，分别在以健康眼睛和患有视网膜病变的眼睛的黄斑中心凹为中心的6×6×1.4毫米和6×6×2毫米体积内获取3D SD-OCT数据。通过对一个可变厚度平面进行采样来生成C模式图像，该平面通过半自动建模以拟合SoI的轮廓。与已发表和商业化的方法不同，该方法不需要视网膜层分割，而视网膜层分割在存在视网膜病变时经常失败。对健康眼睛可视化了四种SoI：视网膜神经纤维（RNF）条纹、视网膜毛细血管网络（RCN）、脉络膜毛细血管网络（CCN）和主要脉络膜血管系统（CV）。对患有视网膜病变的眼睛可视化了各种SoI。
对七只健康眼睛和七只患有视网膜病变的眼睛（黄斑囊样水肿、中心性浆液性视网膜病变、玻璃体视网膜牵拉和年龄相关性黄斑变性）进行了成像。所有眼睛均成功可视化了CCN和CV，而所有健康眼睛以及42.8%的患有病变的眼睛均可视化了RNF和RCN。在所有患有视网膜病变的眼睛中均成功可视化了各种SoI。
所提出的C模式轮廓建模即使在分割算法失败的严重病理变化的眼睛中也可能提供临床上有用的SoI图像。
