Birefringence characterization of biological tissue using optical coherence tomography

1998 ◽  
Author(s):  
Matthew J. Everett ◽  
Klaus Schoenenberger ◽  
Bill W. Colston, Jr. ◽  
Luiz B. Da Silva
Keyword(s):  
Optical Coherence Tomography ◽  
Biological Tissue ◽  
Optical Coherence

Birefringence characterization of biological tissue by use of optical coherence tomography

1998 ◽  
Vol 23 (3) ◽  
pp. 228 ◽  
Author(s):  
M. J. Everett ◽  
K. Schoenenberger ◽  
B. W. Colston ◽  
L. B. Da Silva
Keyword(s):  
Optical Coherence Tomography ◽  
Biological Tissue ◽  
Optical Coherence

scholarly journals Morpho-molecular ex vivo detection and grading of non-muscle-invasive bladder cancer using forward imaging probe based multimodal optical coherence tomography and Raman spectroscopy

The Analyst ◽  
2020 ◽  
Vol 145 (4) ◽  
pp. 1445-1456 ◽  
Author(s):  
Fabian Placzek ◽  
Eliana Cordero Bautista ◽  
Simon Kretschmer ◽  
Lara M. Wurster ◽  
Florian Knorr ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Optical Coherence Tomography ◽  
Imaging System ◽  
Ex Vivo ◽  
Large Field ◽  
Optical Coherence ◽  
Fiber Optic Probe ◽  
Optic Probe ◽  
Muscle Invasive

Characterization of bladder biopsies, using a combined fiber optic probe-based optical coherence tomography and Raman spectroscopy imaging system that allows a large field-of-view imaging and detection and grading of cancerous bladder lesions.

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

2021 ◽  
Vol 11 (7) ◽  
pp. 3119
Author(s):  
Cristina L. Saratxaga ◽  
Jorge Bote ◽  
Juan F. Ortega-Morán ◽  
Artzai Picón ◽  
Elena Terradillos ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Data Augmentation ◽  
Optical Biopsy ◽  
Colon Polyps ◽  
Optical Coherence ◽  
Colon Lesion ◽  
Patient Prognosis ◽  
B Scan Images

(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.

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