Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

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dc.contributor.authorSaratxaga, Cristina L.
dc.contributor.authorBote, Jorge
dc.contributor.authorOrtega-Morán, Juan F.
dc.contributor.authorPicón, Artzai
dc.contributor.authorTerradillos, Elena
dc.contributor.authordel Río, Nagore Arbide
dc.contributor.authorAndraka, Nagore
dc.contributor.authorGarrote, Estibaliz
dc.contributor.authorConde, Olga M.
dc.contributor.institutionVISUAL
dc.contributor.institutionCOMPUTER_VISION
dc.contributor.institutionQuantum
dc.date.issued2021-04-01
dc.descriptionPublisher Copyright: © 2021 by the authors.
dc.description.abstract(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (_0.0141) sensitivity and 0.8094 (_0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (_0.0197) sensitivity and 0.7865 (_0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.en
dc.description.statusPeer reviewed
dc.format.extent1
dc.format.extent2358366
dc.identifier.citationSaratxaga , C L , Bote , J , Ortega-Morán , J F , Picón , A , Terradillos , E , del Río , N A , Andraka , N , Garrote , E & Conde , O M 2021 , ' Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning ' , Applied Sciences , vol. 11 , no. 7 , 3119 , pp. 3119 . https://doi.org/10.3390/app11073119
dc.identifier.doi10.3390/app11073119
dc.identifier.issn2076-3417
dc.identifier.otherresearchoutputwizard: 11556/1106
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85103589197&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofApplied Sciences
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsColon cancer
dc.subject.keywordsColon polyps
dc.subject.keywordsOCT
dc.subject.keywordsDeep learning
dc.subject.keywordsOptical biopsy
dc.subject.keywordsAnimal rat models
dc.subject.keywordsCADx
dc.subject.keywordsColon cancer
dc.subject.keywordsColon polyps
dc.subject.keywordsOCT
dc.subject.keywordsDeep learning
dc.subject.keywordsOptical biopsy
dc.subject.keywordsAnimal rat models
dc.subject.keywordsCADx
dc.subject.keywordsGeneral Materials Science
dc.subject.keywordsInstrumentation
dc.subject.keywordsGeneral Engineering
dc.subject.keywordsProcess Chemistry and Technology
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsFluid Flow and Transfer Processes
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/732111/EU/Multimodal highly-sensitive PhotonICs endoscope for improved in-vivo COLOn Cancer diagnosis and clinical decision support/PICCOLO
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/732111/EU/Multimodal highly-sensitive PhotonICs endoscope for improved in-vivo COLOn Cancer diagnosis and clinical decision support/PICCOLO
dc.subject.keywordsFunding Info
dc.subject.keywordsThis work was partially supported by PICCOLO project. This project has received funding_x000D_ from the European Union’s Horizon2020 Research and Innovation Programme under grant agreement No. 732111. _x000D_ This research has also received funding from the Basque Government’s Industry Department under the ELKARTEK_x000D_ program’s project ONKOTOOLS under agreement KK-2020/00069 and the industrial doctorate program UC- DI14 of the University of Cantabria.
dc.subject.keywordsThis work was partially supported by PICCOLO project. This project has received funding_x000D_ from the European Union’s Horizon2020 Research and Innovation Programme under grant agreement No. 732111. _x000D_ This research has also received funding from the Basque Government’s Industry Department under the ELKARTEK_x000D_ program’s project ONKOTOOLS under agreement KK-2020/00069 and the industrial doctorate program UC- DI14 of the University of Cantabria.
dc.titleCharacterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learningen
dc.typejournal article
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