Department of Imaging Physics, Delft University of Technology, 2628 CN, Delft, The Netherlands.
Rotterdam Eye Hospital, 3011 BH, Rotterdam, The Netherlands.
Sci Rep. 2022 Aug 18;12(1):14035. doi: 10.1038/s41598-022-18180-1.
Corneal guttae, which are the abnormal growth of extracellular matrix in the corneal endothelium, are observed in specular images as black droplets that occlude the endothelial cells. To estimate the corneal parameters (endothelial cell density [ECD], coefficient of variation [CV], and hexagonality [HEX]), we propose a new deep learning method that includes a novel attention mechanism (named fNLA), which helps to infer the cell edges in the occluded areas. The approach first derives the cell edges, then infers the well-detected cells, and finally employs a postprocessing method to fix mistakes. This results in a binary segmentation from which the corneal parameters are estimated. We analyzed 1203 images (500 contained guttae) obtained with a Topcon SP-1P microscope. To generate the ground truth, we performed manual segmentation in all images. Several networks were evaluated (UNet, ResUNeXt, DenseUNets, UNet++, etc.) and we found that DenseUNets with fNLA provided the lowest error: a mean absolute error of 23.16 [cells/mm[Formula: see text]] in ECD, 1.28 [%] in CV, and 3.13 [%] in HEX. Compared with Topcon's built-in software, our error was 3-6 times smaller. Overall, our approach handled notably well the cells affected by guttae, detecting cell edges partially occluded by small guttae and discarding large areas covered by extensive guttae.
角膜滴状突起是角膜内皮细胞外基质的异常生长，在共焦图像中表现为黑色的液滴状，会遮挡内皮细胞。为了评估角膜参数（内皮细胞密度[ECD]、变异系数[CV]和六边形[HEX]），我们提出了一种新的深度学习方法，包括一种新的注意力机制（命名为 fNLA），有助于推断被遮挡区域的细胞边缘。该方法首先推导出细胞边缘，然后推断出被很好检测到的细胞，最后采用后处理方法来修正错误。这导致了一个二进制分割，从中可以估计角膜参数。我们分析了 1203 张使用 Topcon SP-1P 显微镜获得的图像（500 张含有滴状突起）。为了生成真实情况，我们对所有图像进行了手动分割。评估了几种网络（UNet、ResUNeXt、DenseUNets、UNet++等），我们发现带有 fNLA 的 DenseUNets 提供了最低的错误：ECD 的平均绝对误差为 23.16 [cells/mm[Formula: see text]]，CV 为 1.28 [%]，HEX 为 3.13 [%]。与 Topcon 内置软件相比，我们的误差小了 3-6 倍。总的来说，我们的方法很好地处理了受滴状突起影响的细胞，检测到了被小滴状突起部分遮挡的细胞边缘，并丢弃了被广泛滴状突起覆盖的大面积区域。
