scholarly journals Reconstruction of visible light optical coherence tomography images retrieved from discontinuous spectral data using a conditional generative adversarial network

2021 ◽  
Author(s):  
Antonia Lichtenegger ◽  
Matthias Salas ◽  
Alexander Sing ◽  
Marcus Duelk ◽  
Roxane Licandro ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Visible Light ◽  
Spectral Data ◽  
Optical Coherence ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network

2019 ◽  
Vol 27 (9) ◽  
pp. 12289 ◽  
Author(s):  
Yongqiang Huang ◽  
Zexin Lu ◽  
Zhimin Shao ◽  
Maosong Ran ◽  
Jiliu Zhou ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Super Resolution ◽  
Optical Coherence ◽  
Generative Adversarial Network ◽  
Adversarial Network

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.

scholarly journals Spectrally dependent roll-off in visible-light optical coherence tomography

2020 ◽  
Vol 45 (9) ◽  
pp. 2680 ◽  
Author(s):  
Ian Rubinoff ◽  
Brian Soetikno ◽  
David A. Miller ◽  
Isabella Rischall ◽  
Amani Fawzi ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Visible Light ◽  
Optical Coherence
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