Glaucoma is a retinal disease caused due to increased intraocular pressure in the eyes. It is the second most dominant cause of irreversible blindness after cataract, and if this remains undiagnosed, it may become the first common cause. Ophthalmologists use different comprehensive retinal examinations such as ophthalmoscopy, tonometry, perimetry, gonioscopy and pachymetry to diagnose glaucoma. But all these approaches are manual and time-consuming. Thus, a computer-aided diagnosis system may aid as an assistive measure for the initial screening of glaucoma for diagnosis purposes, thereby reducing the computational complexity. This paper presents a deep learning-based disc cup segmentation glaucoma network (DC-Gnet) for the extraction of structural features namely cup-to-disc ratio, disc damage likelihood scale and inferior superior nasal temporal regions for diagnosis of glaucoma. The proposed approach of segmentation has been tested on RIM-One and Drishti-GS dataset. Further, based on experimental analysis, the DC-Gnet is found to outperform U-net, Gnet and Deep-lab architectures.

中文翻译：

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DC-Gnet在眼底成像中检测青光眼

青光眼是一种由于眼内眼压升高引起的视网膜疾病。它是白内障后不可逆性失明的第二大主要原因，如果仍然无法诊断，可能会成为第一常见原因。眼科医生使用各种全面的视网膜检查，例如检眼镜，眼压计，视野检查，测角镜和测厚法来诊断青光眼。但是所有这些方法都是手动且耗时的。因此，计算机辅助诊断系统可以作为用于诊断的青光眼的初始筛查的辅助措施，从而降低计算复杂度。本文提出了一种基于深度学习的碟形杯分割青光眼网络（DC-Gnet），用于提取结构特征（即杯碟比），椎间盘损伤可能性量表和鼻下颞叶区域用于青光眼的诊断。提议的分割方法已在RIM-One和Drishti-GS数据集上进行了测试。此外，基于实验分析，发现DC-Gnet的性能优于U-net，Gnet和Deep-lab架构。