Wan Cheng, Shao Yiwei, Wang Chenghu, Jing Jiaona, Yang Weihua
College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Department of Ophthalmology, Nanjing Lishui Hospital of Traditional Chinese Medicine, Nanjing, China.
Front Med (Lausanne). 2022 Feb 14;9:819971. doi: 10.3389/fmed.2022.819971. eCollection 2022.
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.
翼状胬肉是一种常见的眼表疾病。当翼状胬肉显著侵入角膜时，会限制眼球运动并损害视力，这就需要通过手术切除。医学上公认，当侵入角膜的翼状胬肉宽度>3mm时，可对患者进行手术切除治疗。基于此，本研究提出一种利用深度学习方法诊断和测量翼状胬肉病理进展的系统，旨在协助医生设计翼状胬肉手术治疗策略。所提出的系统只需输入患者的眼前节图像，就能自动、高效地测量侵入角膜的翼状胬肉宽度，并获取患者的翼状胬肉症状状态。该系统由三个模块组成，包括角膜分割模块、翼状胬肉分割模块和测量模块。两个分割模块均使用卷积神经网络。在翼状胬肉分割模块中，为适应翼状胬肉形状和大小的多样性，提出一种在每个上采样层之前添加注意力门的改进U-Net++模型。注意力门提取与目标相关的信息，使模型能够更加关注翼状胬肉的形状和大小。测量模块实现对侵入角膜的翼状胬肉宽度和面积的测量以及翼状胬肉症状状态的分类。在本研究中，利用从南京医科大学附属眼科医院眼表疾病中心收集的数据集验证了所提系统的有效性。所得结果表明，角膜分割模块和翼状胬肉分割模块的Dice系数分别为0.9620和0.9020。系统最终测量结果与医生目视检查结果之间的Kappa一致性系数为0.918，证明该系统具有实际应用意义。
