Guiping Qian

Communication University of Zhejiang

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Mingyu Xu

Institute of Automation

Yiming Sun

Second Affiliated Hospital of Zhejiang University

Nuliqiman Maimaiti

Second Affiliated Hospital of Zhejiang University

Juan Ye

Second Affiliated Hospital of Zhejiang University

Jingxuan Cai

Nanjing University

Pengjie Chen

First Affiliated Hospital Zhejiang University

Gangyong Jia

Hangzhou Dianzi University

Peng Jin

Zhengzhou University

Peifang Xu

Second Affiliated Hospital of Zhejiang University

Qing Wu

Hangzhou Dianzi University

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Second Affiliated Hospital of Zhejiang University

Zhejiang University

Hangzhou Dianzi University

University of Science and Technology of China

University of Chinese Academy of Sciences

Sun Yat-sen University

Southwest Petroleum University

Institute of Automation

Institute of Software

Huazhong University of Science and Technology

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An AS-OCT image dataset for deep learning-enabled segmentation and 3D reconstruction for keratitis

Scientific Data (2024)

Yiming Sun Nuliqiman Maimaiti Peifang Xu Peng Jin Jingxuan Cai

Abstract Infectious keratitis is among the major causes of global blindness. Anterior segment optical coherence tomography (AS-OCT) images allow the characterizing of cross-sectional structures in the cornea with keratitis thus revealing the severity of inflammation, and can also provide 360-degree information on anterior chambers. The development of image analysis methods for such cases, particularly deep learning methods, requires a large number of annotated images, but to date, there is no such open-access AS-OCT image repository. For this reason, this work provides a dataset containing a total of 1168 AS-OCT images of patients with keratitis, including 768 full-frame images (6 patients). Each image has associated segmentation labels for lesions and cornea, and also labels of iris for full-frame images. This study provides a great opportunity to advance the field of image analysis on AS-OCT images in both two-dimensional (2D) and three-dimensional (3D) and would aid in the development of artificial intelligence-based keratitis management.

10.1038/s41597-024-03464-0

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