A Deep Reinforcement Learning Framework for Frame-by-Frame Plaque Tracking on Intravascular Optical Coherence Tomography Image

2019 ◽  
pp. 12-20
Author(s):  
Gongning Luo ◽  
Suyu Dong ◽  
Kuanquan Wang ◽  
Dong Zhang ◽  
Yue Gao ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Reinforcement Learning ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Learning Framework ◽  
Tomography Image ◽  
Intravascular Optical Coherence Tomography

Safety and Feasibility of an Intravascular Optical Coherence Tomography Image Wire System in the Clinical Setting

2008 ◽  
Vol 101 (5) ◽  
pp. 562-567 ◽  
Author(s):  
Tetsu Yamaguchi ◽  
Mitsuyasu Terashima ◽  
Takashi Akasaka ◽  
Takahiro Hayashi ◽  
Kyoichi Mizuno ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Clinical Setting ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image ◽  
Intravascular Optical Coherence Tomography ◽  
Wire System

scholarly journals Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.

Optical coherence tomography image analysis of polymer surface layers in sound-absorbing fibrous composite materials

2017 ◽  
Vol 63 ◽  
pp. 194-203 ◽  
Author(s):  
Eulalia Gliścińska ◽  
Dominik Sankowski ◽  
Izabella Krucińska ◽  
Jarosław Gocławski ◽  
Marina Michalak ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Composite Materials ◽  
Fibrous Composite ◽  
Polymer Surface ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Surface Layers ◽  
Tomography Image

scholarly journals Optical coherence tomography image in gelatinous drop-like corneal dystrophy: case report

2012 ◽  
Vol 75 (5) ◽  
pp. 356-357 ◽  
Author(s):  
Otávio de Azevedo Magalhães ◽  
Samuel Rymer ◽  
Diane Ruschel Marinho ◽  
Sérgio Kwitko ◽  
Isabel Habeyche Cardoso ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Case Report ◽  
Corneal Dystrophy ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image
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