Sah Raman Prasad, Patel Nimesh B, Queener Hope M, Narra Pavan K, Ostrin Lisa A
University of Houston College of Optometry, 4401 MLK Blvd, Houston, TX 77204, USA.
University of Houston College of Optometry, 4401 MLK Blvd, Houston, TX 77204, USA.
J Optom. 2025 Apr-Jun;18(2):100556. doi: 10.1016/j.optom.2025.100556. Epub 2025 May 5.
To develop and validate a custom deep learning-based automated segmentation for choroidal thickness of optical coherence tomography (OCT) scans.
An in-house automated algorithm was trained on a Deeplabv3+ network, based on ResNet50, using a training set of 10,798 manually segmented OCT scans (accuracy 99.25% and loss 0.0229). A test set of 130 unique scans was segmented using manual and in-house automated methods. For manual segmentation, the choroid-sclera border was delineated by the user. For in-house automated segmentation, all borders were automatically detected by the program and manually inspected. Bland-Altman analysis, intraclass correlation coefficient (ICC), and Deming regression compared the central 1-mm diameter and 3-mm and 6-mm annuli for the two methods. The in-house method was also compared with an open-source algorithm for the test set of 130 scans.
Mean choroidal thicknesses obtained with manual and in-house automated methods were not significantly different for the three regions (P > 0.05 for all). The fixed bias between methods ranged from -2.41 to 3.49 µm. Proportional bias ranged from -0.04 to -0.12 (P < 0.05 for all). The two methods demonstrated excellent agreement across regions (ICC: 0.96 to 0.98, P < 0.001 for all). The open-source automated method consistently resulted in thinner choroidal thickness compared to manual and in-house automated methods.
Custom in-house deep learning automated choroid segmentation demonstrated excellent agreement and strong positive linear relationship with manual segmentation. The automated approach holds distinct advantages for estimating choroidal thickness, being more objective and efficient than the manual approach.
开发并验证一种基于深度学习的自定义自动分割方法，用于光学相干断层扫描（OCT）图像的脉络膜厚度测量。
基于ResNet50的Deeplabv3+网络上训练了一种内部自动算法，使用了10798张手动分割的OCT扫描图像作为训练集（准确率99.25%，损失0.0229）。使用手动和内部自动方法对130张独特扫描图像的测试集进行分割。对于手动分割，由用户勾勒脉络膜-巩膜边界。对于内部自动分割，程序自动检测所有边界并进行人工检查。采用Bland-Altman分析、组内相关系数（ICC）和Deming回归比较两种方法在中央1毫米直径区域以及3毫米和6毫米环形区域的测量结果。还将内部方法与一种开源算法对130张扫描图像的测试集进行了比较。
手动和内部自动方法在三个区域获得的平均脉络膜厚度无显著差异（所有P>0.05）。两种方法之间的固定偏差范围为-2.41至3.49微米。比例偏差范围为-0.04至-0.12（所有P<0.05）。两种方法在各区域均显示出极好的一致性（ICC：0.96至0.98，所有P<0.001）。与手动和内部自动方法相比，开源自动方法得到的脉络膜厚度始终较薄。
自定义的内部深度学习自动脉络膜分割方法与手动分割显示出极好的一致性和强正线性关系。该自动方法在估计脉络膜厚度方面具有明显优势，比手动方法更客观、高效。
