Optical biopsy of tissue with Mueller polarimetry: theory and experiments (Conference Presentation)

The possibilities of using optical spectroscopy methods in the differential diagnosis of prostate cancer were investigated. Analytical discrimination models of Raman spectra of prostate tissue were constructed by using the projections onto latent structures data analysis(PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers (FASN, SREBP1, stearoyl-CoA desaturase, etc.), and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid. The presence of an ensemble of Raman peaks with increased intensity, inherent in fatty acid, beta-glucose, glutamic acid, and cholesterol, is a fundamental factor for the identification of prostate cancer.

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

Applied Sciences ◽

10.3390/app11073119 ◽

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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Turbulent thermal diffusion in strongly stratified turbulence: Theory and experiments

Physical Review Fluids ◽

10.1103/physrevfluids.2.064605 ◽

2017 ◽

Vol 2 (6) ◽

Cited By ~ 4

Author(s):

G. Amir ◽

N. Bar ◽

A. Eidelman ◽

T. Elperin ◽

N. Kleeorin ◽

...

Keyword(s):

Thermal Diffusion ◽

Turbulence Theory ◽

Stratified Turbulence ◽

Theory And Experiments

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