基于改进U-Net的视盘视杯联合分割方法
Optic disk and cup joint segmentation network based on improved U-Net
周利涛 1王志超 1施璜浩 1常珊1
作者信息
- 1. 江苏理工学院电气信息工程学院生物信息与医药工程研究所,常州 213001
- 折叠
摘要
青光眼是一种不可逆的致盲性眼疾,疾病早期症状不明显使得许多患者错失治疗的最佳时机.眼底照相作为最常见的青光眼筛查手段,眼底杯盘比值是诊断青光眼的重要指标之一.针对图像中视盘视杯分割精度不高的问题,构建了一种改进U-Net的视盘视杯联合分割模型CASSP-Net,引入CBAM注意力机制和空洞空间金字塔结构,进一步提升视盘视杯联合分割的精确度,在Drishti-GS和REFUGE数据集中进行测试,在Dice和IoU上分别获得92.03%和85.23%的较好表现.
Abstract
Glaucoma is an irreversible blinding eye disease.The early symptoms of the disease are not obvious,causing many patients to miss the best opportunity for treatment.Fundus photography is a common glaucoma screening method,and the fundus cup-to-disc ratio is one of the important indicators for diagnosing glaucoma.In order to solve the problem of low optic disc and optic cup segmentation accuracy in images,an improved U-Net optic disc and optic cup joint segmentation model CASSP-Net was con-structed.The CBAM attention mechanism and hole space pyramid structure were introduced to further improve the optic disc and optic cup joint segmentation.The accuracy was tested in the Drishti-GS and REFUGE data sets,and achieved good performance of 92.03%and 85.23%on Dice and IoU respectively.
关键词
青光眼/视盘/视杯/眼底图像分割/深度学习Key words
glaucoma/optic disc/optic cup/fundus image segmentation/deep learning引用本文复制引用
基金项目
国家自然科学基金青年基金(81603152)
常州市科技支撑计划(社会发展)(CE20205033)
出版年
2024
NETL
NSTL
万方数据