Noninvasive Imaging of Living Human Skin with Dual-Wavelength Optical Coherence Tomography in Two and Three Dimensions

1998 ◽  
Vol 3 (4) ◽  
pp. 446 ◽  
Author(s):  
Yingtian Pan
Keyword(s):  
Optical Coherence Tomography ◽  
Human Skin ◽  
Noninvasive Imaging ◽  
Three Dimensions ◽  
Dual Wavelength ◽  
Optical Coherence

Birefringence and vascular imaging of in vivo human skin by Jones-matrix optical coherence tomography

2017 ◽  
Author(s):  
En Li ◽  
Shuichi Makita ◽  
Young-Joo Hong ◽  
Deepa Kasaragod ◽  
Yoshiaki Yasuno
Keyword(s):  
Optical Coherence Tomography ◽  
Human Skin ◽  
Vascular Imaging ◽  
Jones Matrix ◽  
Optical Coherence

Noninvasive imaging of heart chamber inDrosophilawith dual-beam optical coherence tomography

2012 ◽  
Vol 6 (9) ◽  
pp. 708-717 ◽  
Author(s):  
Meng-Tsan Tsai ◽  
Cheng-Kuang Lee ◽  
Feng-Yu Chang ◽  
June-Tai Wu ◽  
Chung-Pu Wu ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Noninvasive Imaging ◽  
Optical Coherence ◽  
Heart Chamber ◽  
Dual Beam

scholarly journals Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography

2014 ◽  
Vol 55 (2) ◽  
pp. 1024 ◽  
Author(s):  
Zhongwei Zhi ◽  
Jennifer R. Chao ◽  
Tomasz Wietecha ◽  
Kelly L. Hudkins ◽  
Charles E. Alpers ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Noninvasive Imaging ◽  
Optical Coherence ◽  
Obese Mice ◽  
Optical Microangiography

scholarly journals Expert-level Automated Biomarker Identification in Optical Coherence Tomography Scans

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas Kurmann ◽  
Siqing Yu ◽  
Pablo Márquez-Neila ◽  
Andreas Ebneter ◽  
Martin Zinkernagel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Critical Role ◽  
Cost Effective ◽  
Automated Analysis ◽  
Machine Learning Algorithms ◽  
Three Dimensions ◽  
Optical Coherence ◽  
Clinical Routine ◽  
Wide Range

Abstract In ophthalmology, retinal biological markers, or biomarkers, play a critical role in the management of chronic eye conditions and in the development of new therapeutics. While many imaging technologies used today can visualize these, Optical Coherence Tomography (OCT) is often the tool of choice due to its ability to image retinal structures in three dimensions at micrometer resolution. But with widespread use in clinical routine, and growing prevalence in chronic retinal conditions, the quantity of scans acquired worldwide is surpassing the capacity of retinal specialists to inspect these in meaningful ways. Instead, automated analysis of scans using machine learning algorithms provide a cost effective and reliable alternative to assist ophthalmologists in clinical routine and research. We present a machine learning method capable of consistently identifying a wide range of common retinal biomarkers from OCT scans. Our approach avoids the need for costly segmentation annotations and allows scans to be characterized by biomarker distributions. These can then be used to classify scans based on their underlying pathology in a device-independent way.

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