Roy Abhijit Guha, Conjeti Sailesh, Karri Sri Phani Krishna, Sheet Debdoot, Katouzian Amin, Wachinger Christian, Navab Nassir
Computer Aided Medical Procedures, Technische Universität München, Munich, Germany.
Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-University, Munich, Germany.
Biomed Opt Express. 2017 Jul 13;8(8):3627-3642. doi: 10.1364/BOE.8.003627. eCollection 2017 Aug 1.
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.
光学相干断层扫描（OCT）用于评估视网膜各层，以对糖尿病性黄斑水肿进行无创诊断。在本文中，我们提出了一种全新的全卷积深度架构，称为ReLayNet，用于对眼部OCT扫描中的视网膜各层和液体积聚进行端到端分割。ReLayNet使用卷积块的收缩路径（编码器）来学习上下文特征层次结构，随后是用于语义分割的卷积块扩展路径（解码器）。ReLayNet经过训练以优化由加权逻辑回归和骰子重叠损失组成的联合损失函数。该框架在一个公开可用的基准数据集上进行了验证，并与包括两种基于深度学习的方法在内的五种最新分割方法进行了比较，以证实其有效性。
