Purpose

The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise segmentation of the optic cup (OC) and optic disc (OD). In recent years, convolution neural networks (CNN) have shown outstanding performance in medical segmentation tasks. However, most CNN-based methods ignore the effect of boundary ambiguity on performance, which leads to low generalization. This paper is dedicated to solving this issue.

Methods

In this paper, we propose a novel segmentation architecture, called BGA-Net, which introduces an auxiliary boundary branch and adversarial learning to jointly segment OD and OC in a multi-label manner. To generate more accurate results, the generative adversarial network is exploited to encourage boundary and mask predictions to be similar to the ground truth ones.

Results

Experimental results show that our BGA-Net system achieves state-of-the-art OC and OD segmentation performance on three publicly available datasets, i.e., the Dice scores for the optic disc/cup on the Drishti-GS, RIM-ONE-r3 and REFUGE datasets are 0.975/0.898, 0.967/0.872 and 0.951/0.866, respectively.

Conclusion

In this work, we not only achieve superior OD and OC segmentation results, but also confirm that the values calculated through the geometric relationship between the former two are highly related to glaucoma.

中文翻译：

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基于边界先验和对抗学习的联合视盘和视杯分割

目的

青光眼筛查的最直接方法是通过彩色眼底照相使用杯与盘的比率，第一步是对视杯（OC）和视盘（OD）进行精确分割。近年来，卷积神经网络（CNN）在医学分割任务中显示了出色的性能。但是，大多数基于CNN的方法都忽略了边界模糊性对性能的影响，这导致泛化率较低。本文致力于解决这个问题。

方法

在本文中，我们提出了一种新颖的分割架构，称为BGA-Net，它引入了辅助边界分支和对抗学习，以多标签方式共​​同分割OD和OC。为了产生更准确的结果，利用生成对抗网络来鼓励边界和掩膜预测与地面真实预测相似。

结果

实验结果表明，我们的BGA-Net系统在三个公开可用的数据集上实现了最先进的OC和OD分割性能，即Drishti-GS，RIM-ONE-r3上光盘/杯的Dice得分和REFUGE数据集分别为0.975 / 0.898、0.967 / 0.872和0.951 / 0.866。

结论

在这项工作中，我们不仅获得了出色的OD和OC分割结果，而且证实了通过前两者之间的几何关系计算出的值与青光眼高度相关。