  Automated segmentation of Cardiac Magnetic Resonance (CMR) plays a pivotal
role in efficiently assessing cardiac function, offering rapid clinical
evaluations that benefit both healthcare practitioners and patients. While
recent research has primarily focused on delineating structures in the
short-axis orientation, less attention has been given to long-axis
representations, mainly due to the complex nature of structures in this
orientation. Performing pixel-wise segmentation of the left ventricular (LV)
myocardium and the four cardiac chambers in 2-D steady-state free precession
(SSFP) cine sequences is a crucial preprocessing stage for various analyses.
However, the challenge lies in the significant variability in contrast,
appearance, orientation, and positioning of the heart across different
patients, clinical views, scanners, and imaging protocols. Consequently,
achieving fully automatic semantic segmentation in this context is notoriously
challenging. In recent years, several deep learning models have been proposed
to accurately quantify and diagnose cardiac pathologies. These automated tools
heavily rely on the accurate segmentation of cardiac structures in magnetic
resonance images (MRI). Hence, there is a need for new methods to handle such
structures' geometrical and textural complexities. We proposed 2D and 3D
two-stage self-supervised deep learning segmentation hybrid transformer and
CNN-based architectures for 4CH whole heart segmentation. Accurate segmentation
of the ventricles and atria in 4CH views is crucial for analyzing heart health
and reconstructing four-chamber meshes, which are essential for estimating
various parameters to assess overall heart condition. Our proposed method
outperformed state-of-the-art techniques, demonstrating superior performance in
this domain.
