Eye Center, Humanitas Gavazzeni-Castelli, Bergamo, Italy.
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4Pieve Emanuele, 20072, Milan, Italy.
Graefes Arch Clin Exp Ophthalmol. 2024 Feb;262(2):431-440. doi: 10.1007/s00417-023-06261-4. Epub 2023 Oct 16.
To assess the validity of the results of a freely available online Deep Learning segmentation tool and its sensitivity to noise introduced by cataract.
The OCT images were collected with a Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) as part of normal clinical practice. Data were segmented using a freely available online tool called Relayer ( https://www.relayer.online/ ), based on a cross-platform Deep Learning segmentation architecture specifically adapted for retinal OCT images. The segmentations were read into MATLAB (The MathWorks, Natick, MA, USA) and analyzed.
There was an excellent agreement between the ETDRS measurements obtained from the two algorithms. Upon visual inspection, the segmentation based on Deep Learning obtained with Relayer appeared more accurate except in one case of apparent good quality image showing interrupted segmentations in some of the B-scans.
A freely available online Deep Learning segmentation tool showed good and promising performance in healthy retinas before and after cataract surgery, proving robust to optical degradation of the image from media opacities.
评估一款免费在线深度学习分割工具的结果的有效性及其对白内障引起的噪声的敏感性。
OCT 图像是作为常规临床实践的一部分使用 Spectralis SD-OCT（德国海德堡工程公司，海德堡）采集的。使用名为 Relayer（https://www.relayer.online/）的免费在线工具对数据进行分割，该工具基于专门为视网膜 OCT 图像设计的跨平台深度学习分割架构。分割结果被读取到 MATLAB（美国马萨诸塞州纳蒂克的 MathWorks）中进行分析。
两种算法得到的 ETDRS 测量值之间具有极好的一致性。通过视觉检查，基于 Relayer 的深度学习分割似乎更准确，除了在一张质量明显良好的图像中，在一些 B 扫描中出现了中断的分割。
一款免费在线深度学习分割工具在白内障手术前后的健康视网膜中表现出良好且有前景的性能，证明其对来自介质混浊的图像光学退化具有稳健性。
