Dept. of Electrical Engg, Indian Institute of Technology Hyderabad, Telangana, India; Dept. of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, USA.
L. V. Prasad Eye Institute, Hyderabad, Telangana, India.
Comput Med Imaging Graph. 2022 Jul;99:102086. doi: 10.1016/j.compmedimag.2022.102086. Epub 2022 Jun 2.
The choroid, a dense vascular structure in the posterior segment of the eye, maintains the health of the retina by supplying oxygen and nutrients, and assumes clinical significance in screening ocular diseases including age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR). As a technological assist, algorithmic estimation of choroidal biomarkers has been suggested based on sectional (B-scan) optical coherence tomography (OCT) images. However, most such 2D estimation techniques are compute-intensive, yet enjoy limited accuracy and have only been validated on OCT image datasets of healthy eyes. Not surprisingly, fine-scale analyses, including those involving Haller's sublayer, remain relatively rare and unsophisticated. Against this backdrop, we propose an efficient algorithm to quantify desired biomarkers with improved accuracy based on volume OCT scans. Specifically, we attempted an accurate, computationally light volumetric segmentation method involving stratified smoothing to detect choroid and Haller's sublayer.
For detecting the various boundaries of the choroid and the Haller's sublayer, we propose a common volumetric method that performs suitable exponential enhancement and maintains smooth spatial continuity across 2D B-scans. Further, we achieve suitable volumetric smoothing by primarily deploying light-duty linear regression, and sparingly using compute-intensive tensor voting, and hence significantly reduce overall complexity. The proposed methodology is tested on five health and five diseased OCT volumes considering various metrics including volumetric Dice coefficient and corresponding quotient measures to facilitate comparison vis-à-vis intra-observer repeatability.
On five healthy and five diseased OCT volumes, respectively, the proposed method for choroid segmentation recorded volumetric Dice coefficients of 93.53 % and 93.30 %, which closely approximate the respective reference observer repeatability values of 95.60 % and 95.49 %. In terms of related quotient measures, our method achieved more than 50 % improvement over a recently reported method. In detecting Haller's sublayer as well, our algorithm records statistical performance closely matching that of reference manual method.
Advancing the state-of-the-art, the proposed volumetric segmentation, tested on both healthy and diseased datasets, demonstrated close match with the manual reference. Our method assumes significance in accurate screening of chorioretinal diseases including AMD, CSCR and pachychoroid. Further, it enables generating accurate training data for developing deep learning models for improved detection of choroid and Haller's sublayer.
脉络膜是眼球后部的密集血管结构，通过供应氧气和营养物质来维持视网膜的健康，在包括年龄相关性黄斑变性（AMD）和中心性浆液性脉络膜视网膜病变（CSCR）在内的眼部疾病筛查中具有重要的临床意义。作为一种技术辅助手段，已经提出了基于节段（B 扫描）光学相干断层扫描（OCT）图像的脉络膜生物标志物的算法估计。然而，大多数这样的 2D 估计技术计算密集，准确性有限，并且仅在健康眼的 OCT 图像数据集上得到验证。毫不奇怪，精细分析，包括涉及 Haller 亚层的分析，仍然相对较少且不成熟。在此背景下，我们提出了一种基于体积 OCT 扫描的高效算法，可以更准确地量化所需的生物标志物。具体来说，我们尝试了一种准确、计算量轻的分层平滑的体积分割方法来检测脉络膜和 Haller 亚层。
为了检测脉络膜和 Haller 亚层的各种边界，我们提出了一种通用的体积方法，该方法对适当的指数增强进行操作，并在 2D B 扫描之间保持平滑的空间连续性。此外，我们通过主要使用轻量级线性回归并适度使用计算密集型张量投票来实现适当的体积平滑，从而显著降低整体复杂度。该方法在考虑各种指标的情况下，包括体积 Dice 系数和相应的商测度，在五个健康和五个患病的 OCT 体数据集上进行了测试，以便与观察者内重复性进行比较。
分别在五个健康和五个患病的 OCT 体数据集上，所提出的脉络膜分割方法记录的体积 Dice 系数为 93.53%和 93.30%，这与各自的参考观察者重复性值 95.60%和 95.49%非常接近。在相关商测度方面，与最近报道的方法相比，我们的方法取得了超过 50%的改进。在检测 Haller 亚层方面，我们的算法记录的统计性能与参考手动方法非常匹配。
该研究提出了一种基于体积的分割方法，该方法在健康和患病数据集上进行了测试，与手动参考方法非常匹配。该方法在包括 AMD、CSCR 和脉络膜肥厚在内的脉络膜视网膜疾病的准确筛查中具有重要意义。此外，它还可以为开发用于提高脉络膜和 Haller 亚层检测的深度学习模型生成准确的训练数据。
