Apostolopoulos, Stefanos; De Zanet, Sandro; Ciller, Carlos; Wolf, Sebastian; Sznitman, Raphael (16 July 2017). Pathological OCT Retinal Layer Segmentation using Branch Residual U-style Networks. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).
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1707.04931.pdf - Published Version Available under License BORIS Standard License. Download (2MB) | Preview |
The automatic segmentation of retinal layer structures enables clinically-relevant quantification and monitoring of eye disorders over time in OCT imaging. Eyes with late-stage diseases are particularly challenging to segment, as their shape is highly warped due to pathological biomarkers. In this context, we propose a novel fully Convolutional Neural Network (CNN) architecture which combines dilated residual blocks in an asymmetric U-shape configuration, and can segment multiple layers of highly pathological eyes in one shot. We validate our approach on a dataset of late-stage AMD patients and demonstrate lower computational costs and higher performance compared to other state-of-the-art methods.
Item Type: |
Conference or Workshop Item (Paper) |
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Division/Institute: |
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory 04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology 10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research |
Graduate School: |
Graduate School for Cellular and Biomedical Sciences (GCB) |
UniBE Contributor: |
Apostolopoulos, Stefanos, Ciller, Carlos, Wolf, Sebastian (B), Sznitman, Raphael |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health 600 Technology > 620 Engineering |
Language: |
English |
Submitter: |
Raphael Sznitman |
Date Deposited: |
01 Mar 2018 11:27 |
Last Modified: |
05 Dec 2022 15:09 |
ArXiv ID: |
1707.04931v1 |
BORIS DOI: |
10.7892/boris.108436 |
URI: |
https://boris.unibe.ch/id/eprint/108436 |
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