Cai Wenjia, Xu Jie, Wang Ke, Liu Xiaohong, Xu Wenqin, Cai Huimin, Gao Yuanxu, Su Yuandong, Zhang Meixia, Zhu Jie, Zhang Charlotte L, Zhang Edward E, Wang Fangfei, Yin Yun, Lai Iat Fan, Wang Guangyu, Zhang Kang, Zheng Yingfeng
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.
Beijing Institute of Ophthalmology, Capital Medical University, Beijing Tongren Hospital, Beijing 100730, China.
Precis Clin Med. 2021 Apr 27;4(2):85-92. doi: 10.1093/pcmedi/pbab009. eCollection 2021 Jun.
Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide, including diseases associated with corneal pathologies, anterior chamber abnormalities (e.g. blood or inflammation), and lens diseases. The construction of an automatic tool for segmentation of anterior segment eye lesions would greatly improve the efficiency of clinical care. With research on artificial intelligence progressing in recent years, deep learning models have shown their superiority in image classification and segmentation. The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise; however, such data are relatively scarce in the domain of medicine. Herein, the authors developed a new medical image annotation system, called EyeHealer. It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level. Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation. The results showed that semantic segmentation models outperformed medical segmentation models. This paper describes the establishment of the system for automated classification and segmentation tasks. The dataset will be made publicly available to encourage future research in this area.
眼前节眼病在全球眼科诊所的就诊病例中占很大比例，包括与角膜病变、前房异常（如出血或炎症）以及晶状体疾病相关的疾病。构建一种用于眼前节眼部病变分割的自动工具将大大提高临床护理效率。近年来随着人工智能研究的进展，深度学习模型在图像分类和分割方面显示出其优越性。深度学习模型的训练和评估应基于大量经专业标注的数据；然而，此类数据在医学领域相对稀缺。在此，作者开发了一种新的医学图像标注系统，称为EyeHealer。它是一个大规模的眼前节数据集，其中眼睛结构和病变均在像素级别进行了标注。进行了全面实验以验证其在疾病分类和眼部病变分割方面的性能。结果表明，语义分割模型优于医学分割模型。本文描述了用于自动分类和分割任务的系统的建立。该数据集将公开提供，以鼓励该领域未来的研究。
