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.

scholarly journals AngioNet: A Convolutional Neural Network for Vessel Segmentation in X-ray Angiography

2021 ◽  
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 ◽  
Stenosis Severity ◽  
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 stenosis severity. 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 which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, sensitivity 0.918, specificity 0.987). 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.

scholarly journals COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

scholarly journals COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

Abstract The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest x-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient's chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 patient cases into a custom network architecture for severity assessment. Experimental results with a multi-national patient cohort curated by the Radiological Society of North America (RSNA) RICORD initiative showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

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.

Abstract 14957: Comparison of Coronary Tree Detection and Diameter Quantification From a Deep Learning Convolutional Neural Network With Quantitative Coronary Angiography

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Kritika Iyer ◽  
Cyrus Najarian ◽  
Mullasari A Sankardas ◽  
Vijayakumar Subban ◽  
Brahmajee K Nallamothu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Coronary Angiography ◽  
Convolutional Neural Network ◽  
Quantitative Coronary Angiography ◽  
Vessel Diameter ◽  
Absolute Difference ◽  
Fast Marching ◽  
Minimum Diameter ◽  
Stenosis Severity

Introduction: QCA (Quantitative Coronary Angiography) is an accepted standard for assessing coronary artery stenosis severity. QCA computes the percentage diameter reduction of the lesion, improving upon visual inspection since it provides a numerical quantification of stenosis severity. However, it can be costly, and it requires human input to correct the boundaries of the vessel. AngioNet is a fully-automatic deep learning convolutional neural network trained to identify coronary trees and their vessel diameters in angiograms, and could be a cost-effective alternative to QCA. To explore the accuracy of AngioNet, we compared its ability to identify vessels and their diameter when compared with QCA. Methods: Angiograms with 3-vessel QCA reports from 89 patients acquired at Madras Medical Mission were used. The entire vessel tree was segmented for each angiogram using AngioNet. A fast-marching method was used to calculate the radius along the vessel’s centerline. Vessel diameter was compared in two regions corresponding to the regions marked in the QCA report: the most proximal region, containing the maximum vessel diameter, and the region of stenosis. If no stenosis was present, the distal region containing minimum diameter was selected. The difference in maximum and minimum vessel diameter between QCA and AngioNet was computed, and a Bland-Altman plot (see figure) was used to determine the interchangeability of both methods. Results: The mean absolute difference between both measurements was 0.27mm. The standardized difference was 0.215, corresponding to a 91.5% overlap of their distributions. Lastly, 97% of data points were within the limits of agreement, suggesting minimal clinical differences and that both methods may be interchangeable. Conclusions: Deep learning convolutional networks may be used to assess vessel diameters at a level that agrees well with those of QCA. AngioNet has the potential to automate the detection of stenosis severity.

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