Spatio-Temporal Multi-Task Learning for Cardiac MRI Left Ventricle Quantification

Spatio-Temporal Multi-Task Learning for Cardiac MRI Left Ventricle Quantification

Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved...

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Bibliographic Details
Main Authors: Gu, M. (Author), Maier, A. (Author), Ravikumar, N. (Author), Vesal, S. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
anticoagulation
Article
artificial neural network
Cardiac MRI
cardiac segmentation
cardiovascular disease
Cardio-vascular disease
cine magnetic resonance imaging
Correlation methods
deep learning
Deep learning
Diagnosis
diagnostic imaging
diastole
entropy
heart
Heart
heart atrium appendage
heart cycle
heart function
heart infarction
heart left ventricle
heart rate
heart ventricle
Heart Ventricles
hippocampus
human
Humans
image quality
Intra-observer variability
kidney cortex
Learning systems
left ventricle quantification
machine learning
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging
Magnetic Resonance Imaging, Cine
Mean absolute error
Morphology
Multi-task learning
myocardial infraction
nonhuman
nuclear magnetic resonance imaging
Pearson correlation coefficients
Phase classification
Prediction accuracy
Quantification methods
quantitative analysis
Quantitative assessments
radiography
Radiography
sudden cardiac death
systole
thickness
warfarin
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