An enhanced hierarchical Mamba-based cardiac segmentation network for cardiovascular analysis and disease diagnosis using multiple MRI datasets

Abstract

Automatic cardiac segmentation grew in popularity as it became feasible in clinical settings. Deep learning-based techniques are used throughout the world to gain access to cardiac functioning. Although the current deep learning architecture represents an important step toward clinical usability, it falls short in terms of accurately and effectively segmenting critical cardiac segments such as the left ventricle (LV), right ventricle (RV), and myocardium. Furthermore, the generalization of approaches is a significant difficulty when doing cardiac segmentation. Therefore, in this article we proposed the Mamba-based algorithm, which we named as H-MayoMamba Net, that uses hierarchical structure to undergo local processing, regional processing, global processing, and context processing in each level, one by one.

The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗