期刊
BIOMEDICAL OPTICS EXPRESS
卷 8, 期 8, 页码 3627-3642出版社
OPTICAL SOC AMER
DOI: 10.1364/BOE.8.003627
关键词
-
资金
- Faculty of Medicine at LMU (FoFoLe)
- Bavarian State Ministry of Education, Science and the Arts
Optical coherence tomography (OCT) is used for non- invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and Dice overlap loss. The framework is validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods including two deep learning based approaches to substantiate its effectiveness. (C) 2017 Optical Society of America
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据