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

• Published in: IEEE journal of biomedical and health informatics Vol. 25; no. 7; pp. 2698 - 2709
• Main Authors: Vesal, Sulaiman, Gu, Mingxuan, Maier, Andreas, Ravikumar, Nishant
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cardiac MRI; cardiac segmentation; Cardiovascular diseases; Coders; Contouring; Correlation coefficient; Correlation coefficients; Deep learning; Diastole; Encoders-Decoders; Feature extraction; Image contrast; Image segmentation; left ventricle quantification; Machine learning; Magnetic resonance imaging; Medical imaging; Morphology; myocardial infraction; Myocardium; Optimization; Systole; Task analysis; Temporal variations; Three-dimensional displays; Training; Two dimensional displays; Ventricle; Wall thickness

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...