ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks

Roy, Abhijit Guha; Conjeti, Sailesh; Karri, Sri Phani Krishna; Sheet, Debdoot; Katouzian, Amin; Wachinger, Christian und Navab, Nassir (2017): ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. In: Biomedical Optics Express, Vol. 8, No. 8: pp. 3627-3642

Abstract

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

• Item Type: Journal article
• Faculties: Psychology and Education Science > Department Psychology
• Subjects: 100 Philosophy and Psychology > 150 Psychology
• ISSN: 2156-7085
• Language: English