Background and Objective

Glaucoma, a worldwide eye disease, may cause irreversible vision damage. If not treated properly at an early stage, glaucoma eventually deteriorates into blindness. Various glaucoma screening methods, e.g. Ultrasound Biomicroscopy (UBM), Optical Coherence Tomography (OCT), and Heidelberg Retinal Scanner (HRT), are available. However, retinal fundus image photography examination, because of its low cost, is one of the most common solutions used to diagnose glaucoma. Clinically, the cup-to-disk ratio is an important indicator in glaucoma diagnosis. Therefore, precise fundus image segmentation to calculate the cup-to-disk ratio is the basis for screening glaucoma.

Methods

In this paper, we propose a deep neural network that uses anatomical knowledge to guide the segmentation of fundus images, which accurately segments the optic cup and the optic disc in a fundus image to accurately calculate the cup-to-disk ratio. Optic disc and optic cup segmentation are typical small target segmentation problems in biomedical images. We propose to use an attention-based cascade network to effectively accelerate the convergence of small target segmentation during training and accurately reserve detailed contours of small targets.

Results

Our method, which was validated in the MICCAI REFUGE fundus image segmentation competition, achieves 93.31% dice score in optic disc segmentation and 88.04% dice score in optic cup segmentation. Moreover, we win a high CDR evaluation score, which is useful for glaucoma screening.

Conclusions

The proposed method successfully introduce anatomical knowledge into segmentation task, and achieve state-of-the-art performance in fundus image segmentation. It also can be used for both automatic segmentation and semiautomatic segmentation with human interaction.

中文翻译：

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基于解剖引导级联网络的视盘和视杯分割。

背景与目的

青光眼是一种全球性的眼病，可能会导致不可逆的视力损害。如果不及早治疗，青光眼最终会恶化为失明。可以使用各种青光眼筛查方法，例如超声生物显微镜（UBM），光学相干断层扫描（OCT）和海德堡视网膜扫描仪（HRT）。然而，视网膜眼底图像摄影检查由于其成本低，是用于诊断青光眼的最常见解决方案之一。临床上，杯盘比是青光眼诊断的重要指标。因此，精确的眼底图像分割以计算杯盘比是筛选青光眼的基础。

方法

在本文中，我们提出了一个深层神经网络，该神经网络利用解剖学知识来指导眼底图像的分割，从而将眼底图像中的视杯和视盘准确地分割，从而准确地计算出杯盘比。光盘和视杯分割是生物医学图像中典型的小目标分割问题。我们建议使用基于注意力的级联网络，以有效地加速训练过程中小目标分割的收敛，并准确保留小目标的详细轮廓。

结果

我们的方法在MICCAI REFUGE眼底图像分割比赛中得到了验证，在视盘分割中骰子得分达到93.31％，在视杯分割中骰子得分达到88.04％。此外，我们获得了较高的CDR评估分数，这对青光眼筛查很有用。

结论

所提出的方法成功地将解剖学知识引入了分割任务，并实现了眼底图像分割的最新技术。它也可以用于具有人类交互作用的自动分割和半自动分割。