Srinivasan Pratul P, Heflin Stephanie J, Izatt Joseph A, Arshavsky Vadim Y, Farsiu Sina
Department of Biomedical Engineering, Duke University, Durham 27708, USA ; Department of Computer Science, Duke University, Durham 27708, USA.
Department of Ophthalmology, Duke University Medical Center, Durham 27710, USA.
Biomed Opt Express. 2014 Jan 7;5(2):348-65. doi: 10.1364/BOE.5.000348. eCollection 2014 Feb 1.
Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software.
在光学相干断层扫描（OCT）中准确量化小鼠视网膜层厚度对于众多眼部和神经疾病的研究至关重要。然而，手动分割既耗时又主观。此前将此过程自动化的尝试仅限于来自没有缺失层或可见病变的小鼠的高质量扫描。本文提出了一种使用基于稀疏性的去噪、支持向量机、图论和动态规划（S-GTDP）在光谱域OCT图像中分割视网膜层的自动方法。结果表明，与手动分割相比，该方法能准确分割野生型和视紫红质基因敲除小鼠中所有存在的视网膜层边界（范围从七层到十层），并且与商业自动化Diver分割软件相比具有更准确的性能。
