Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156185
Title: Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images
Authors: Cahyo, Dheo A. Y.
Yow, Ai Ping
Saw, Seang-Mei
Ang, Marcus
Girard, Michael
Schmetterer, Leopold
Wong, Damon Wing Kee
Keywords: Engineering::Chemical engineering
Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
Issue Date: 2021
Source: Cahyo, D. A. Y., Yow, A. P., Saw, S., Ang, M., Girard, M., Schmetterer, L. & Wong, D. W. K. (2021). Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images. Biomedical Optics Express, 12(12), 7348-7360. https://dx.doi.org/10.1364/BOE.428140
Project: CG/C010A/2017_SERI 
MOH-000249-00 
MOH-OFIRG20nov-0014 
OFIRG/0048/2017 
OFLCG/004c/2018 
TA/MOH-000249-00/2018 
NRF2019-THE002-0006 
NRF-CRP24-2020-0001 
A20H4b0141 
LF1019-1 
Duke-NUS-KP(Coll)/2018/0009A 
Journal: Biomedical Optics Express 
Abstract: The choroid is the vascular layer of the eye that supplies photoreceptors with oxygen. Changes in the choroid are associated with many pathologies including myopia where the choroid progressively thins due to axial elongation. To quantize these changes, there is a need to automatically and accurately segment the choroidal layer from optical coherence tomography (OCT) images. In this paper, we propose a multi-task learning approach to segment the choroid from three-dimensional OCT images. Our proposed architecture aggregates the spatial context from adjacent cross-sectional slices to reconstruct the central slice. Spatial context learned by this reconstruction mechanism is then fused with a U-Net based architecture for segmentation. The proposed approach was evaluated on volumetric OCT scans of 166 myopic eyes acquired with a commercial OCT system, and achieved a cross-validation Intersection over Union (IoU) score of 94.69% which significantly outperformed (p<0.001) the other state-of-the-art methods on the same data set. Choroidal thickness maps generated by our approach also achieved a better structural similarity index (SSIM) of 72.11% with respect to the groundtruth. In particular, our approach performs well for highly challenging eyes with thinner choroids. Compared to other methods, our proposed approach also requires lesser processing time and has lower computational requirements. The results suggest that our proposed approach could potentially be used as a fast and reliable method for automated choroidal segmentation.
URI: https://hdl.handle.net/10356/156185
ISSN: 2156-7085
DOI: 10.1364/BOE.428140
Rights: © 2021 Optical Society of America under the terms of the Open Access Publishing Agreement. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for noncommercial purposes and appropriate attribution is maintained. All other rights are reserved.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IDMxS Journal Articles
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