Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets-Reference-Cited by-同舟云学术

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Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets

Published:2015-10-22 Issue:10 Volume:17 Page:1118-1127
ISSN:2047-2404
Container-title:European Heart Journal – Cardiovascular Imaging
language:en
Short-container-title:Eur Heart J Cardiovasc Imaging

Author:

Queirós Sandro,Barbosa Daniel,Engvall Jan,Ebbers Tino,Nagel Eike,Sarvari Sebastian I.,Claus Piet,Fonseca Jaime C.,Vilaça João L.,D'hooge Jan

Funder

FCT—Fundação para a Ciência e a Tecnologia

EU (FP7) framework program

Publisher

Oxford University Press (OUP)

Subject

Cardiology and Cardiovascular Medicine,Radiology Nuclear Medicine and imaging,General Medicine

Link

http://academic.oup.com/ehjcimaging/article-pdf/17/10/1118/7080324/jev247.pdf

Reference41 articles.

1. Comparison of real-time three-dimensional echocardiography with cardiovascular magnetic resonance for left ventricular volumetric assessment in unselected patients

2. Quantification of left ventricular indices from SSFP cine imaging: impact of real-world variability in analysis methodology and utility of geometric modeling;Miller;J Magn Reson Imaging,2012

3. A review of segmentation methods in short axis cardiac MR images

4. Accurate computer-aided quantification of left ventricular parameters: Experience in 1555 cardiac magnetic resonance studies from the Framingham Heart Study

5. Normal Cardiac Magnetic Resonance Measurements and Interobserver Discrepancies in Volumes and Mass Using the Papillary Muscle Inclusion Method

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1. Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data;Scientific Reports;2024-08-14

2. Multivendor Evaluation of Automated MRI Postprocessing of Biventricular Size and Function for Children With and Without Congenital Heart Defects;Journal of Magnetic Resonance Imaging;2022-12-26

3. Right Ventricular Segmentation in Multi-view Cardiac MRI Using a Unified U-net Model;Lecture Notes in Computer Science;2022

4. Balancing Speed and Accuracy in Cardiac Magnetic Resonance Function Post-Processing: Comparing 2 Levels of Automation in 3 Vendors to Manual Assessment;Diagnostics;2021-09-24

5. Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging;The International Journal of Cardiovascular Imaging;2021-06-29

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