Vessel Segmentation and Stenosis Quantification from Coronary X-Ray Angiograms

2020 ◽  
pp. 27-34
Author(s):  
Irina Andra Tache ◽  
Dimitrios Glotsos
Keyword(s):  
Vessel Segmentation ◽  
X Ray ◽  
Stenosis Quantification

Vessel Segmentation of X-Ray Coronary Angiographic Image Sequence

2020 ◽  
Vol 67 (5) ◽  
pp. 1338-1348 ◽  
Author(s):  
Shaoyan Xia ◽  
Haogang Zhu ◽  
Xiaoli Liu ◽  
Ming Gong ◽  
Xiaoyong Huang ◽  
...  
Keyword(s):  
Image Sequence ◽  
Vessel Segmentation ◽  
X Ray ◽  
Angiographic Image

scholarly journals AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kritika Iyer ◽  
Cyrus P. Najarian ◽  
Aya A. Fattah ◽  
Christopher J. Arthurs ◽  
S. M. Reza Soroushmehr ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Coronary Vessels ◽  
Quantitative Coronary Angiography ◽  
Vessel Diameter ◽  
Vessel Segmentation ◽  
Clinical Workflow ◽  
X Ray ◽  
Multiple Network ◽  
Diameter Reduction

AbstractCoronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.

X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing

2021 ◽  
Author(s):  
E O Rodrigues ◽  
L. O. Rodrigues ◽  
J. J. Lima ◽  
D. Casanova ◽  
F. Favarim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Region Growing ◽  
Vessel Segmentation ◽  
Pixel Classification ◽  
X Ray

Direction- and Centerline-Aware Joint Learning Network (JLNet) for Vessel Segmentation in X-Ray Angiography Images

2021 ◽  
Author(s):  
Tao Han ◽  
Yonglin Bian ◽  
Ruirui An ◽  
Yechen Han ◽  
Danni Ai ◽  
...  
Keyword(s):  
Vessel Segmentation ◽  
Joint Learning ◽  
X Ray ◽  
Learning Network

Vessel segmentation in low contrast X-ray angiogram images

2016 ◽  
Author(s):  
B. Felfelian ◽  
H. R. Fazlali ◽  
N. Karimi ◽  
S. M. R. Soroushmehr ◽  
S. Samavi ◽  
...  
Keyword(s):  
Vessel Segmentation ◽  
Low Contrast ◽  
X Ray

Evaluation of a new method for stenosis quantification from 3D x-ray angiography images

2001 ◽  
Author(s):  
Fabienne Betting ◽  
Gilles Moris ◽  
Jerome Knoplioch ◽  
Yves L. Trousset ◽  
Francisco Sureda ◽  
...  
Keyword(s):  
New Method ◽  
X Ray ◽  
Stenosis Quantification

scholarly journals Deep learning segmentation of major vessels in X-ray coronary angiography

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Su Yang ◽  
Jihoon Kweon ◽  
Jae-Hyung Roh ◽  
Jae-Hwan Lee ◽  
Heejun Kang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Coronary Arteries ◽  
Coronary Vessels ◽  
Quantitative Coronary Angiography ◽  
Vessel Segmentation ◽  
Diagnostic Methods ◽  
X Ray ◽  
Fully Convolutional Networks ◽  
Major Vessel

AbstractX-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

Sign in / Sign up

Export Citation Format

Share Document