A Novel System for Measuring Pterygium's Progress Using Deep Learning

Cheng Wan; Yiwei Shao; Chenghu Wang; Jiaona Jing; Weihua Yang

doi:10.3389/fmed.2022.819971

A Novel System for Measuring Pterygium's Progress Using Deep Learning

Front Med (Lausanne). 2022 Feb 14:9:819971. doi: 10.3389/fmed.2022.819971. eCollection 2022.

Authors

Cheng Wan¹, Yiwei Shao¹, Chenghu Wang^{2

3}, Jiaona Jing⁴, Weihua Yang³

Affiliations

¹ College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
² Department of Ophthalmology, Nanjing Lishui Hospital of Traditional Chinese Medicine, Nanjing, China.
³ The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China.
⁴ Department of Ophthalmology, Children's Hospital of Nanjing Medical University, Nanjing, China.

Abstract

Pterygium is a common ocular surface disease. When pterygium significantly invades the cornea, it limits eye movement and impairs vision, which requires surgery to remove. It is medically recognized that when the width of the pterygium that invades the cornea is >3 mm, the patient can be treated with surgical resection. Owing to this, this study proposes a system for diagnosing and measuring the pathological progress of pterygium using deep learning methods, which aims to assist doctors in designing pterygium surgical treatment strategies. The proposed system only needs to input the anterior segment images of patients to automatically and efficiently measure the width of the pterygium that invades the cornea, and the patient's pterygium symptom status can be obtained. The system consists of three modules, including cornea segmentation module, pterygium segmentation module, and measurement module. Both segmentation modules use convolutional neural networks. In the pterygium segmentation module, to adapt the diversity of the pterygium's shape and size, an improved U-Net++ model by adding an Attention gate before each up-sampling layer is proposed. The Attention gates extract information related to the target, so that the model can pay more attention to the shape and size of the pterygium. The measurement module realizes the measurement of the width and area of the pterygium that invades the cornea and the classification of pterygium symptom status. In this study, the effectiveness of the proposed system is verified using datasets collected from the ocular surface diseases center at the Affiliated Eye Hospital of Nanjing Medical University. The results obtained show that the Dice coefficient of the cornea segmentation module and the pterygium segmentation module are 0.9620 and 0.9020, respectively. The Kappa consistency coefficient between the final measurement results of the system and the doctor's visual inspection results is 0.918, which proves that the system has practical application significance.

Keywords: chi-square test; computer-aided diagnosis; deep learning; image segmentation; pterygium.