Zhou Hao, Liu Jeremy, Laiginhas Rita, Zhang Qinqin, Cheng Yuxuan, Zhang Yi, Shi Yingying, Shen Mengxi, Gregori Giovanni, Rosenfeld Philip J, Wang Ruikang K
Department of Bioengineering, University of Washington, Seattle, WA 98105, USA.
Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Biomed Opt Express. 2022 Jul 7;13(8):4175-4189. doi: 10.1364/BOE.467623. eCollection 2022 Aug 1.
An automated depth-resolved algorithm using optical attenuation coefficients (OACs) was developed to visualize, localize, and quantify hyperreflective foci (HRF) seen on OCT imaging that are associated with macular hyperpigmentation and represent an increased risk of disease progression in age related macular degeneration. To achieve this, we first transformed the OCT scans to linear representation, which were then contrasted by OACs. HRF were visualized and localized within the entire scan by differentiating HRF within the retina from HRF along the retinal pigment epithelium (RPE). The total pigment burden was quantified using the sum projection of an OAC slab between the inner limiting membrane (ILM) to Bruch's membrane (BM). The manual total pigment burden measurements were also obtained by combining manual outlines of HRF in the B-scans with the total area of hypotransmission defects outlined on sub-RPE slabs, which was used as the reference to compare with those obtained from the automated algorithm. 6×6 mm swept-source OCT scans were collected from a total of 49 eyes from 42 patients with macular HRF. We demonstrate that the algorithm was able to automatically distinguish between HRF within the retina and HRF along the RPE. In 24 test eyes, the total pigment burden measurements by the automated algorithm were compared with measurements obtained from manual segmentations. A significant correlation was found between the total pigment area measurements from the automated and manual segmentations (P < 0.001). The proposed automated algorithm based on OACs should be useful in studying eye diseases involving HRF.
开发了一种使用光学衰减系数（OAC）的自动深度分辨算法，以可视化、定位和量化在光学相干断层扫描（OCT）成像中看到的高反射灶（HRF），这些高反射灶与黄斑色素沉着有关，并且代表年龄相关性黄斑变性疾病进展风险增加。为实现这一目标，我们首先将OCT扫描转换为线性表示，然后用OAC进行对比。通过区分视网膜内的HRF和沿视网膜色素上皮（RPE）的HRF，在整个扫描中可视化并定位HRF。使用从内界膜（ILM）到布鲁赫膜（BM）之间的OAC平板的总和投影来量化总色素负荷。还通过将B扫描中HRF的手动轮廓与RPE下平板上勾勒出的低透射缺陷的总面积相结合来获得手动总色素负荷测量值，该值用作与自动算法获得的值进行比较的参考。从42例患有黄斑HRF的患者的49只眼中收集了6×6 mm扫频源OCT扫描。我们证明该算法能够自动区分视网膜内的HRF和沿RPE的HRF。在24只测试眼中，将自动算法测量的总色素负荷与手动分割获得的测量值进行了比较。自动分割和手动分割的总色素面积测量值之间存在显著相关性（P < 0.001）。所提出的基于OAC的自动算法在研究涉及HRF的眼部疾病中应该是有用的。
