RT Journal Article
T1 Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning
A1 Saratxaga, Cristina L.
A1 Bote, Jorge
A1 Ortega-Morán, Juan F.
A1 Picón, Artzai
A1 Terradillos, Elena
A1 del Río, Nagore Arbide
A1 Andraka, Nagore
A1 Garrote, Estibaliz
A1 Conde, Olga M.
AB (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.
SN 2076-3417
YR 2021
FD 2021-04-01
LA eng
NO Saratxaga , 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
NO Publisher Copyright: © 2021 by the authors.
DS TECNALIA Publications
RD 3 jul 2024