Convolutional Neural Networks for Cardiac MRI Analysis: An Overview

Abstract

Cardiac magnetic resonance imaging (cardiac MRI) provides detailed information about cardiac anatomy and function, but
analysing a large number of images can be time-consuming when performed manually. Deep learning methods, particularly convolutional
neural networks (CNNs), are widely used for automated medical image analysis. This paper provides an overview of
CNNs and their applications in cardiac MRI analysis, with a focus on cardiac image segmentation. The paper first presents the
basic principles of CNNs, including convolution, pooling and activation functions, followed by a discussion of U-Net and its use
in biomedical image segmentation. Applications of CNNs in cardiac MRI, including cardiac structure segmentation, disease classification
and quantitative assessment, are also discussed. Recent developments such as 2D and 3D CNNs, attention mechanisms
and hybrid CNN-Transformer architectures are presented. The paper also discusses current challenges, including limited annotated
datasets, dierences between imaging settings, generalization, interpretability and clinical validation. Overall, CNNs remain important
tools for cardiac MRI analysis, while recent research is exploring more robust models that can be applied across dierent
clinical settings.