Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156185
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dc.contributor.authorCahyo, Dheo A. Y.en_US
dc.contributor.authorYow, Ai Pingen_US
dc.contributor.authorSaw, Seang-Meien_US
dc.contributor.authorAng, Marcusen_US
dc.contributor.authorGirard, Michaelen_US
dc.contributor.authorSchmetterer, Leopolden_US
dc.contributor.authorWong, Damon Wing Keeen_US
dc.date.accessioned2022-04-11T01:31:55Z-
dc.date.available2022-04-11T01:31:55Z-
dc.date.issued2021-
dc.identifier.citationCahyo, 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.428140en_US
dc.identifier.issn2156-7085en_US
dc.identifier.urihttps://hdl.handle.net/10356/156185-
dc.description.abstractThe 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.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Medical Research Council (NMRC)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationCG/C010A/2017_SERIen_US
dc.relationMOH-000249-00en_US
dc.relationMOH-OFIRG20nov-0014en_US
dc.relationOFIRG/0048/2017en_US
dc.relationOFLCG/004c/2018en_US
dc.relationTA/MOH-000249-00/2018en_US
dc.relationNRF2019-THE002-0006en_US
dc.relationNRF-CRP24-2020-0001en_US
dc.relationA20H4b0141en_US
dc.relationLF1019-1en_US
dc.relationDuke-NUS-KP(Coll)/2018/0009Aen_US
dc.relation.ispartofBiomedical Optics Expressen_US
dc.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.en_US
dc.subjectEngineering::Chemical engineeringen_US
dc.subjectEngineering::Electrical and electronic engineering::Optics, optoelectronics, photonicsen_US
dc.titleMulti-task learning approach for volumetric segmentation and reconstruction in 3D OCT imagesen_US
dc.typeJournal Article-
dc.contributor.schoolSchool of Chemical and Biomedical Engineeringen_US
dc.contributor.organizationSingapore National Eye Centreen_US
dc.contributor.researchInstitute for Digital Molecular Analytics and Science (IDMxS)en_US
dc.contributor.researchSingapore Centre for Environmental Life Sciences and Engineering (SCELSE)en_US
dc.contributor.researchSERI-NTU Advanced Ocular Engineering (STANCE)en_US
dc.identifier.doi10.1364/BOE.428140-
dc.description.versionPublished versionen_US
dc.identifier.pmid35003838-
dc.identifier.scopus2-s2.0-85120003267-
dc.identifier.issue12en_US
dc.identifier.volume12en_US
dc.identifier.spage7348en_US
dc.identifier.epage7360en_US
dc.subject.keywordsImage Reconstructionen_US
dc.subject.keywordsOptical Tomographyen_US
dc.description.acknowledgementNational Medical Research Council (CG/C010A/2017_SERI, MOH-000249-00, MOH-OFIRG20nov-0014, OFIRG/0048/2017, OFLCG/004c/2018, TA/MOH-000249-00/2018); Singapore Eye Research Institute & Nanyang Technological University (SERI-NTU Advanced Ocular Engineering Program); National Research Foundation Singapore (NRF2019-THE002-0006, NRF-CRP24-2020-0001); Agency for Science, Technology and Research (A20H4b0141); SERI-Lee Foundation (LF1019-1); Duke-NUS Medical School (Duke-NUS-KP(Coll)/2018/0009A).en_US
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