scholarly journals In vivo label-free lymphangiography of cutaneous lymphatic vessels in human burn scars using optical coherence tomography

2016 ◽  
Vol 7 (12) ◽  
pp. 4886 ◽  
Author(s):  
Peijun Gong ◽  
Shaghayegh Es’haghian ◽  
Karl-Anton Harms ◽  
Alexandra Murray ◽  
Suzanne Rea ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Lymphatic Vessels ◽  
Optical Coherence ◽  
Label Free

scholarly journals Label-Free In Vivo Imaging of Corneal Lymphatic Vessels Using Microscopic Optical Coherence Tomography

2017 ◽  
Vol 58 (13) ◽  
pp. 5880 ◽  
Author(s):  
Jens Horstmann ◽  
Hinnerk Schulz-Hildebrandt ◽  
Felix Bock ◽  
Sebastian Siebelmann ◽  
Eva Lankenau ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
In Vivo Imaging ◽  
Lymphatic Vessels ◽  
Optical Coherence ◽  
Label Free

scholarly journals In vivo label-free measurement of lymph flow velocity and volumetric flow rates using Doppler optical coherence tomography

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Cedric Blatter ◽  
Eelco F. J. Meijer ◽  
Ahhyun S. Nam ◽  
Dennis Jones ◽  
Brett E. Bouma ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Flow Velocity ◽  
Lymph Flow ◽  
Optical Coherence ◽  
Label Free ◽  
Flow Rates ◽  
Doppler Optical Coherence Tomography

scholarly journals In vivo, label-free, three-dimensional quantitative imaging of kidney microcirculation using Doppler optical coherence tomography

2011 ◽  
Vol 91 (11) ◽  
pp. 1596-1604 ◽  
Author(s):  
Jeremiah Wierwille ◽  
Peter M Andrews ◽  
Maristela L Onozato ◽  
James Jiang ◽  
Alex Cable ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Quantitative Imaging ◽  
Three Dimensional ◽  
Optical Coherence ◽  
Label Free ◽  
Doppler Optical Coherence Tomography

scholarly journals OCT2Hist: Non-Invasive Virtual Biopsy Using Optical Coherence Tomography

2021 ◽  
Author(s):  
Yonatan Winetraub ◽  
Edwin Yuan ◽  
Itamar Terem ◽  
Caroline Yu ◽  
Warren Chan ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Ground Truth ◽  
Disease Diagnosis ◽  
Optical Coherence ◽  
Label Free ◽  
Generative Adversarial Network ◽  
Non Invasive ◽  
Adversarial Network ◽  
Trained Neural Network

Histological haematoxylin and eosin–stained (H&E) tissue sections are used as the gold standard for pathologic detection of cancer, tumour margin detection, and disease diagnosis1. Producing H&E sections, however, is invasive and time-consuming. Non-invasive optical imaging modalities, such as optical coherence tomography (OCT), permit label-free, micron-scale 3D imaging of biological tissue microstructure with significant depth (up to 1mm) and large fields-of-view2, but are difficult to interpret and correlate with clinical ground truth without specialized training3. Here we introduce the concept of a virtual biopsy, using generative neural networks to synthesize virtual H&E sections from OCT images. To do so we have developed a novel technique, “optical barcoding”, which has allowed us to repeatedly extract the 2D OCT slice from a 3D OCT volume that corresponds to a given H&E tissue section, with very high alignment precision down to 25 microns. Using 1,005 prospectively collected human skin sections from Mohs surgery operations of 71 patients, we constructed the largest dataset of H&E images and their corresponding precisely aligned OCT images, and trained a conditional generative adversarial network4 on these image pairs. Our results demonstrate the ability to use OCT images to generate high-fidelity virtual H&E sections and entire 3D H&E volumes. Applying this trained neural network to in vivo OCT images should enable physicians to readily incorporate OCT imaging into their clinical practice, reducing the number of unnecessary biopsy procedures.

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