High-speed automatic segmentation of intravascular stent struts in optical coherence tomography images

2013 ◽  
Author(s):  
M. Han ◽  
D. Kim ◽  
W. Y. Oh ◽  
S. Ryu
Keyword(s):  
Optical Coherence Tomography ◽  
High Speed ◽  
Automatic Segmentation ◽  
Optical Coherence ◽  
Intravascular Stent

scholarly journals Laser Lens Size Measurement Using Swept-Source Optical Coherence Tomography

2020 ◽  
Vol 10 (14) ◽  
pp. 4936
Author(s):  
Pingping Jia ◽  
Hong Zhao ◽  
Yuwei Qin
Keyword(s):  
Optical Coherence Tomography ◽  
High Speed ◽  
Wave Number ◽  
Radius Of Curvature ◽  
Optical Coherence ◽  
Swept Source ◽  
Laser Focusing ◽  
Fitting Algorithm ◽  
Laser Confocal Microscope ◽  
Focusing Lens

A high-speed, high-resolution swept-source optical coherence tomography (SS-OCT) is presented for focusing lens imaging and a k-domain uniform algorithm is adopted to find the wave number phase equalization. The radius of curvature of the laser focusing lens was obtained using a curve-fitting algorithm. The experimental results demonstrate that the measuring accuracy of the proposed SS-OCT system is higher than the laser confocal microscope. The SS-OCT system has great potential for surface topography measurement and defect inspection of the focusing lens.

scholarly journals Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography

2021 ◽  
Vol 11 (12) ◽  
pp. 5488
Author(s):  
Wei Ping Hsia ◽  
Siu Lun Tse ◽  
Chia Jen Chang ◽  
Yu Len Huang
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Choroidal Thickness ◽  
Automatic Segmentation ◽  
Average Error ◽  
Good Prediction ◽  
Optical Coherence ◽  
Learning Method ◽  
Subfoveal Choroidal Thickness ◽  
Fully Connected

The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 ∩ R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid.

scholarly journals Two-axis magnetically-driven MEMS scanning catheter for endoscopic high-speed optical coherence tomography

2007 ◽  
Vol 15 (26) ◽  
pp. 18130 ◽  
Author(s):  
Ki Hean Kim ◽  
B. Hyle Park ◽  
Gopi N. Maguluri ◽  
Tom W. Lee ◽  
Fran J. Rogomentich ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
High Speed ◽  
Optical Coherence

In vivo imaging with high-speed full-range complex spectral domain optical coherence tomography

2005 ◽  
Author(s):  
Erich Goetzinger ◽  
Michael Pircher ◽  
Rainer A. Leitgeb ◽  
Adolf F. Fercher ◽  
Christoph K. Hitzenberger
Keyword(s):  
Optical Coherence Tomography ◽  
In Vivo Imaging ◽  
High Speed ◽  
Full Range ◽  
Spectral Domain ◽  
Optical Coherence
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