Zhao Aidi, Su Hong, She Chongyang, Huang Xiao, Li Hui, Qiu Huaiyu, Jiang Zhihong, Huang Gao
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China; Advanced Innovation Center for Intelligent Robots and Systems, Beijing, 100081, China; Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education, Beijing, 100081, China.
Ophthalmology Department, Beijing Chao-Yang hospital, Capital medical university, Beijing, 100020, China.
Comput Biol Med. 2023 May;158:106796. doi: 10.1016/j.compbiomed.2023.106796. Epub 2023 Mar 23.
Glaucoma is a chronic degenerative disease that is the second leading cause of irreversible blindness worldwide. For a precise and automatic screening of glaucoma, detecting the optic disc and cup precisely is significant. In this paper, combining the elliptical-like morphological features of the disc and cup, we reformulate the segmentation task from a perspective of ellipse detection to explicitly segment and directly get the glaucoma screening indicator. We detect the minimum bounding boxes of ellipses firstly, and then learn the ellipse parameters of these regions to achieve optic disc and cup segmentation. Considering the spatial geometry prior knowledge that the cup should be within the disc region, Paired-Box RPN is introduced to simultaneously detect the disc and cup coupled. In addition, boundary attention module is introduced to use edges of the disc and cup as an important guide for context aggregation to improve the accuracy. Comprehensive experiments clearly show that our method outperforms the state-of-the-art methods for optic disc and cup segmentation. Simultaneously, the proposed method also obtains the good glaucoma screening performance with calculated vCDR value. Joint optic disc and cup segmentation, which utilizes the elliptical-like morphological features and spatial geometry constraint, could improve the performance of optic disc and cup segmentation.
青光眼是一种慢性退行性疾病，是全球不可逆失明的第二大主要原因。对于青光眼的精确自动筛查，精确检测视盘和视杯具有重要意义。在本文中，结合视盘和视杯的类椭圆形态特征，我们从椭圆检测的角度重新制定分割任务，以明确分割并直接获得青光眼筛查指标。我们首先检测椭圆的最小外接矩形，然后学习这些区域的椭圆参数以实现视盘和视杯分割。考虑到视杯应在视盘区域内的空间几何先验知识，引入了配对框区域提议网络（Paired-Box RPN）来同时检测耦合的视盘和视杯。此外，引入边界注意力模块，将视盘和视杯的边缘用作上下文聚合的重要指导，以提高准确性。综合实验清楚地表明，我们的方法在视盘和视杯分割方面优于现有方法。同时，所提出的方法通过计算垂直杯盘比（vCDR）值也获得了良好的青光眼筛查性能。利用类椭圆形态特征和空间几何约束的联合视盘和视杯分割可以提高视盘和视杯分割的性能。
