Open AccessResidual learning: A new paradigm to improve deep learning-based segmentation of the left ventricle in magnetic resonance imaging cardiac images
Author(s) -
Maral Zarvani,
Sara Saberi,
Reza Azmi,
Seyed Vahab Shojaedini
Publication year - 2021
Publication title -
journal of medical signals and sensors
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.337
H-Index - 21
ISSN - 2228-7477
DOI - 10.4103/jmss.jmss_38_20
Subject(s) - deep learning , segmentation , artificial intelligence , residual , computer science , ventricle , jaccard index , magnetic resonance imaging , pattern recognition (psychology) , machine learning , algorithm , radiology , medicine , cardiology
Recently, magnetic resonance imaging (MRI) has become a useful tool for the early detection of heart failure. A vital step of this process is a valid measurement of the left ventricle's properties, which seriously depends on the accurate segmentation of the heart in captured images. Although various schemes have been tested for this segmentation so far, the latest proposed methods have used the concept of deep learning to estimate the range of the left ventricle in cardiac MRI images. While deep learning methods can lead to better results than their classical alternatives, but unfortunately, the gradient vanishing and exploding problems may hamper their efficiency for the accurate segmentation of the left ventricle in MRI heart images.