Saratxaga, Cristina L.Bote, JorgeOrtega-Morán, Juan F.Picón, ArtzaiTerradillos, Elenadel Río, Nagore ArbideAndraka, NagoreGarrote, EstibalizConde, Olga M.2021-04-01Saratxaga , 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/app110731192076-3417researchoutputwizard: 11556/1106Publisher Copyright: © 2021 by the authors.(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.12358366enginfo:eu-repo/semantics/openAccessjournal article10.3390/app11073119Colon cancerColon polypsOCTDeep learningOptical biopsyAnimal rat modelsCADxColon cancerColon polypsOCTDeep learningOptical biopsyAnimal rat modelsCADxGeneral Materials ScienceInstrumentationGeneral EngineeringProcess Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer ProcessesSDG 3 - Good Health and Well-beingProject IDinfo:eu-repo/grantAgreement/EC/H2020/732111/EU/Multimodal highly-sensitive PhotonICs endoscope for improved in-vivo COLOn Cancer diagnosis and clinical decision support/PICCOLOinfo:eu-repo/grantAgreement/EC/H2020/732111/EU/Multimodal highly-sensitive PhotonICs endoscope for improved in-vivo COLOn Cancer diagnosis and clinical decision support/PICCOLOFunding InfoThis 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.This 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.http://www.scopus.com/inward/record.url?scp=85103589197&partnerID=8YFLogxK