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Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks

Roy AG, Conjeti S, Karri SPK, Sheet D, Katouzian A, Wachinger C, Navab N (2017)

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Publication Type: Journal article

Publication year: 2017

Journal

Book Volume: 8

Pages Range: 3627-3642

Article Number: #295759

Journal Issue: 8

DOI: 10.1364/BOE.8.003627

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.

Involved external institutions

How to cite

APA:

Roy, A.G., Conjeti, S., Karri, S.P.K., Sheet, D., Katouzian, A., Wachinger, C., & Navab, N. (2017). Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomedical Optics Express, 8(8), 3627-3642. https://doi.org/10.1364/BOE.8.003627

MLA:

Roy, Abhijit Guha, et al. "Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks." Biomedical Optics Express 8.8 (2017): 3627-3642.

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