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.
Highlights
Spectral Domain Optical Coherence Tomography (SD-Optical coherence tomography (OCT)) is a non-invasive imaging modality commonly used for acquiring high resolution (6μm) cross-sectional scans for biological tissues with sufficient depth of penetration (0.5 − 2 mm) [1, 2]
The performance of the proposed ReLayNet is evaluated against state-of-the-art retinal OCT layer segmentation algorithms, Graph based dynamic programming (GDP) [13] (CMGDP), Kernel regression with GDP [15] (CM-KR) and Layer specific structured edge learning with GDP [20](CM-LSE)
We have proposed ReLayNet, an end-to-end fully convolutional framework for semantic segmentation of retinal OCT B-scan into 7 retinal layers and fluid masses
Summary
Spectral Domain Optical Coherence Tomography (SD-OCT) is a non-invasive imaging modality commonly used for acquiring high resolution (6μm) cross-sectional scans for biological tissues with sufficient depth of penetration (0.5 − 2 mm) [1, 2]. It uses the principle of speckle formation through coherence sensing of photons backscattered within highly scattering optical media like biological soft tissues [3]. Proper monitoring of the retinal layer morphology and fluid accumulation is necessary for diabetic patients to prevent chances of occurrence of blindness
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