TY  - Journal Article
AU  - Saratxaga, Cristina L.
AU  - Bote, Jorge
AU  - Ortega-Morán, Juan F.
AU  - Picón, Artzai
AU  - Terradillos, Elena
AU  - del Río, Nagore Arbide
AU  - Andraka, Nagore
AU  - Garrote, Estibaliz
AU  - Conde, Olga M.
TI  - Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning
PY  - 2021
PB  - Multidisciplinary Digital Publishing Institute (MDPI)
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.
UR  - http://hdl.handle.net/11556/1106
DX  - 10.3390/app11073119
ER  -