New fiber-based approaches for optical biopsy (Conference Presentation)

2017 ◽  
Author(s):  
Jessie R. Weber ◽  
Christophe Rivière ◽  
Antoine Proulx ◽  
Pascal Gallant ◽  
Ozzy Mermut
Keyword(s):  
Optical Biopsy

scholarly journals Using the Method of “Optical Biopsy” of Prostatic Tissue to Diagnose Prostate Cancer

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1961
Author(s):  
Dmitry N. Artemyev ◽  
Vladimir I. Kukushkin ◽  
Sofia T. Avraamova ◽  
Nikolay S. Aleksandrov ◽  
Yuri A. Kirillov
Keyword(s):  
Prostate Cancer ◽  
Glutamic Acid ◽  
Raman Spectra ◽  
Aerobic Glycolysis ◽  
Exciting Radiation ◽  
Optical Biopsy ◽  
Strong Increase ◽  
Fundamental Factor ◽  
Diagnose Prostate Cancer ◽  
Latent Structures

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.

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.

scholarly journals 1700 nm optical coherence microscopy enables minimally invasive, label-free, in vivo optical biopsy deep in the mouse brain

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jun Zhu ◽  
Hercules Rezende Freitas ◽  
Izumi Maezawa ◽  
Lee-way Jin ◽  
Vivek J. Srinivasan
Keyword(s):  
Minimally Invasive ◽  
Mouse Brain ◽  
Numerical Aperture ◽  
Optical Biopsy ◽  
Optical Coherence ◽  
Label Free ◽  
Optical Coherence Microscopy ◽  
Minimal Invasiveness ◽  
Cortical Regions

AbstractIn vivo, minimally invasive microscopy in deep cortical and sub-cortical regions of the mouse brain has been challenging. To address this challenge, we present an in vivo high numerical aperture optical coherence microscopy (OCM) approach that fully utilizes the water absorption window around 1700 nm, where ballistic attenuation in the brain is minimized. Key issues, including detector noise, excess light source noise, chromatic dispersion, and the resolution-speckle tradeoff, are analyzed and optimized. Imaging through a thinned-skull preparation that preserves intracranial space, we present volumetric imaging of cytoarchitecture and myeloarchitecture across the entire depth of the mouse neocortex, and some sub-cortical regions. In an Alzheimer’s disease model, we report that findings in superficial and deep cortical layers diverge, highlighting the importance of deep optical biopsy. Compared to other microscopic techniques, our 1700 nm OCM approach achieves a unique combination of intrinsic contrast, minimal invasiveness, and high resolution for deep brain imaging.

Optical Biopsy at the Bedside

2010 ◽  
Vol 146 (8) ◽  
Author(s):  
John Strasswimmer
Keyword(s):  
Optical Biopsy

Development of the optical biopsy system for small experimental animals

2006 ◽  
Author(s):  
Hidetoshi Sato ◽  
Yusuke Hattori ◽  
Yusuke Oshima ◽  
Yuichi Komachi ◽  
Takashi Katagiri ◽  
...  
Keyword(s):  
Optical Biopsy ◽  
Experimental Animals

scholarly journals Optical Biopsy Using a Neural Network to Predict Gene Expression From Photos of Wounds

2022 ◽  
Vol 270 ◽  
pp. 547-554
Author(s):  
Grant Schumaker ◽  
Andrew Becker ◽  
Gary An ◽  
Stephen Badylak ◽  
Scott Johnson ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Optical Biopsy ◽  
Predict Gene Expression
Sign in / Sign up

Export Citation Format

Share Document