Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning
Author/s
Saratxaga, Cristina L.; Bote, Jorge; Ortega-Morán, Juan F.; Picón, Artzai; Terradillos, Elena; [et al.]Date
2021-04-01Keywords
Colon cancer
Colon polyps
OCT
Deep learning
Optical biopsy
Animal rat models
CADx
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 ...
Type
article