Extraction of media adventitia and luminal intima borders by reconstructing intravascular ultrasound image sequences with vascular structural continuity

2021 ◽  
Author(s):  
Yi Huang ◽  
Menghua Xia ◽  
Yi Guo ◽  
Guohui Zhou ◽  
Yuanyuan Wang
Keyword(s):  
Intravascular Ultrasound ◽  
Ultrasound Image ◽  
Image Sequences ◽  
Structural Continuity

Automated segmentation of coronary wall and plaque from intravascular ultrasound image sequences

2002 ◽  
Author(s):  
M. Sonka ◽  
Xiangmin Zhang ◽  
M. Siebes ◽  
S. Dejong ◽  
C.R. McKay ◽  
...  
Keyword(s):  
Intravascular Ultrasound ◽  
Ultrasound Image ◽  
Image Sequences ◽  
Automated Segmentation ◽  
Coronary Wall

Automated 3D-stent localization from intravascular ultrasound image sequences

2000 ◽  
Author(s):  
Michael Schmauder ◽  
Steffen Zeiler ◽  
C. M. Gross ◽  
Juergen Waigand ◽  
Reinhold Orglmeister
Keyword(s):  
Intravascular Ultrasound ◽  
Ultrasound Image ◽  
Image Sequences

Development of an innovative technology to segment luminal borders of intravascular ultrasound image sequences in a fully automated manner

2019 ◽  
Vol 108 ◽  
pp. 111-121 ◽  
Author(s):  
Abouzar Moshfegh ◽  
Ashkan Javadzadegan ◽  
Maryam Mohammadi ◽  
Lakshitha Ravipudi ◽  
Shaokoon Cheng ◽  
...  
Keyword(s):  
Intravascular Ultrasound ◽  
Ultrasound Image ◽  
Image Sequences ◽  
Innovative Technology

An Upgraded Siamese Neural Network for Motion Tracking in Ultrasound Image Sequences

2021 ◽  
pp. 1-1
Author(s):  
Skanda Bharadwaj ◽  
Sumukha Prasad ◽  
Mohamed Almekkawy
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Ultrasound Image ◽  
Image Sequences

scholarly journals 3D Wiener Filtering to Reduce Reverberations in Ultrasound Image Sequences

2003 ◽  
pp. 579-586 ◽  
Author(s):  
Nina Eriksson Bylund ◽  
Marcus Ressner ◽  
Hans Knutsson
Keyword(s):  
Ultrasound Image ◽  
Image Sequences ◽  
Wiener Filtering

scholarly journals Calcification detection using convolutional neural network architectures in Intravascular ultrasound images

2020 ◽  
Vol 17 (3) ◽  
pp. 1313
Author(s):  
Hannah Sofian ◽  
Joel Than Chia Ming ◽  
Suraya Muhammad ◽  
Norliza Mohd Noor
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Coronary Artery ◽  
Convolutional Neural Network ◽  
Intravascular Ultrasound ◽  
Predictive Value ◽  
Area Under Curve ◽  
Ultrasound Image ◽  
Network Architectures ◽  
Neural Network Architectures

<p>Cardiovascular disease is the highest leading to death for Non-Communicable disease. Coronary artery calcification disease is part of cardiovascular disease. The built-in of the plaques and the calcification in the coronary artery inner wall make the blood vessel cross-section area narrow. The standard practice by the radiologists and medical clinical are by visual inspection to detect the calcification in the intravascular ultrasound image. Deep learning is the current image processing methods that have high potential to detect calcification analysis using convolutional neural network architecture and classifiers. To detect the absence of calcification and presence calcification on the intravascular ultrasound image, using k-fold =10, we compared the three types of convolutional neural network architectures and the seven types of classifiers with the provided ground truth from MICCAI 2011. We used two types of images named as Cartesian Coordinates image and polar reconstructed coordinate image. The classifiers such as Support Vector Machine, Discriminant analysis, Ensembles and Error-Correcting Output Codes obtained the perfect result with value one for Area Under Curve and all the performance measure result, accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Area Under Curve for Naïve Bayes classifier is 0.9967 and for Decision Tree classifier is 0.9994, obtained using the polar reconstructed coordinate image for InceptionresNet-V2 architecture.</p>

Motion estimation for ultrasound image sequences using deep learning

2020 ◽  
Vol 148 (4) ◽  
pp. 2487-2487
Author(s):  
Skanda Bharadwaj ◽  
Mohamed Almekkawy
Keyword(s):  
Deep Learning ◽  
Motion Estimation ◽  
Ultrasound Image ◽  
Image Sequences
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