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|Title:||Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images||Authors:||Cahyo, Dheo A. Y.
Yow, Ai Ping
Wong, Damon Wing Kee
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
|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|
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