Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation

TL;DR

This paper introduces a novel self-supervised deep learning approach combining transformers and CNNs for accurate 4-chamber heart segmentation in MRI, addressing variability challenges and improving upon existing methods.

Contribution

It presents a new hybrid transformer-CNN architecture for 2D and 3D self-supervised segmentation of the whole heart in 4-chamber views, outperforming current state-of-the-art models.

Findings

Superior segmentation accuracy over existing methods

Effective handling of variability in MRI images

Enhanced reconstruction of four-chamber heart meshes

Abstract

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.