arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1809.03239cs.CV
收藏

多背景深度网络用于眼前节闭角型青光眼筛查的OCT

Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT

Institute for Infocomm Research, A*STAR(新加坡科技研究局资讯通信研究院);Guangzhou Shiyuan Electronic Technology Company Limited(广州视源电子科技股份有限公司);Microsoft Research(微软研究院);Singapore Eye Research Institute(新加坡眼科研究院);Cixi Institute of Biomedical Engineering, CAS(中国科学院慈溪生物医学工程研究所)

Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Baskaran Mani, Meenakshi Mahesh, Tin Aung, Jiang Liu

摘要:

不可逆视力损害的一个主要原因是闭角型青光眼,它可以通过前段光学相干断层扫描(AS-OCT)的图像进行筛查。以前的计算诊断技术通过从图像中提取特定的临床测量值或手工制作的视觉特征进行分类来解决这个筛查问题。在本文中,我们建议从训练数据中学习一个有区别的表示,它可以捕捉到预定义特征没有建模的细微视觉线索。基于临床先验,我们用提出的多上下文深度网络(MCDN)架构来制定这种学习,其中并行卷积神经网络被应用于特定图像区域,并且在已知为临床诊断闭角型青光眼提供信息的相应尺度下。并行流的输出特征图被合并到分类层中以产生深度筛选结果。此外,我们将估计的临床参数,以进一步提高性能。在临床AS-OCT数据集上,我们的系统通过与以前的筛选方法进行比较进行了验证。

英文摘要:

A major cause of irreversible visual impairment is angle-closure glaucoma, which can be screened through imagery from Anterior Segment Optical Coherence Tomography (AS-OCT). Previous computational diagnostic techniques address this screening problem by extracting specific clinical measurements or handcrafted visual features from the images for classification. In this paper, we instead propose to learn from training data a discriminative representation that may capture subtle visual cues not modeled by predefined features. Based on clinical priors, we formulate this learning with a presented Multi-Context Deep Network (MCDN) architecture, in which parallel Convolutional Neural Networks are applied to particular image regions and at corresponding scales known to be informative for clinically diagnosing angle-closure glaucoma. The output feature maps of the parallel streams are merged into a classification layer to produce the deep screening result. Moreover, we incorporate estimated clinical parameters to further enhance performance. On a clinical AS-OCT dataset, our system is validated through comparisons to previous screening methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑