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, Bd. 8, Nr. 8: S. 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

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Psychologie und Pädagogik > Department Psychologie
• Themengebiete: 100 Philosophie und Psychologie > 150 Psychologie
• ISSN: 2156-7085
• Sprache: Englisch