Li Kaiwen, Yang Guangqian, Chang Shuimiao, Yao Jinhan, He Chong, Lu Fang, Wang Xiaogang, Wang Zhao
School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China.
Department of Cataract, Shanxi Eye Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Biomed Opt Express. 2023 Jul 10;14(8):3968-3987. doi: 10.1364/BOE.493065. eCollection 2023 Aug 1.
Anterior segment diseases are among the leading causes of irreversible blindness. However, a method capable of recognizing all important anterior segment structures for clinical diagnosis is lacking. By sharing the knowledge learned from each task, we proposed a fully automated multitask deep learning method that allows for simultaneous segmentation and quantification of all major anterior segment structures, including the iris, lens, cornea, as well as implantable collamer lens (ICL) and intraocular lens (IOL), and meanwhile for landmark detection of scleral spur and iris root in anterior segment OCT (AS-OCT) images. In addition, we proposed a refraction correction method to correct for the true geometry of the anterior segment distorted by light refraction during OCT imaging. 1251 AS-OCT images from 180 patients were collected and were used to train and test the model. Experiments demonstrated that our proposed network was superior to state-of-the-art segmentation and landmark detection methods, and close agreement was achieved between manually and automatically computed clinical parameters associated with anterior chamber, pupil, iris, ICL, and IOL. Finally, as an example, we demonstrated how our proposed method can be applied to facilitate the clinical evaluation of cataract surgery.
眼前节疾病是不可逆失明的主要原因之一。然而，目前缺乏一种能够识别所有重要眼前节结构以用于临床诊断的方法。通过共享从每个任务中学到的知识，我们提出了一种全自动多任务深度学习方法，该方法能够同时对所有主要眼前节结构进行分割和量化，包括虹膜、晶状体、角膜，以及可植入式角膜接触镜（ICL）和人工晶状体（IOL），同时还能在前节光学相干断层扫描（AS-OCT）图像中进行巩膜突和虹膜根部的地标检测。此外，我们还提出了一种屈光校正方法，以校正OCT成像过程中因光折射而扭曲的眼前节真实几何形状。收集了180名患者的1251张AS-OCT图像，并用于训练和测试该模型。实验表明，我们提出的网络优于现有的分割和地标检测方法，并且手动计算和自动计算的与前房、瞳孔、虹膜、ICL和IOL相关的临床参数之间达成了高度一致。最后，作为一个例子，我们展示了我们提出的方法如何应用于促进白内障手术的临床评估。
