Scientific Reports· 2026Q1
An enhanced hierarchical Mamba-based cardiac segmentation network for cardiovascular analysis and disease diagnosis using multiple MRI datasets
- 0citations
- Q1SCImago
- 2026year
Short summary
A new Mamba-based network, H-MayoMamba Net, achieves enhanced accuracy in segmenting critical cardiac structures (LV, RV, myocardium) from MRI, addressing limitations of current deep learning methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes H-MayoMamba Net, a Mamba-based algorithm for cardiac MRI segmentation.
- Utilizes a hierarchical structure for multi-level processing (local, regional, global, context).
- Aims to improve segmentation accuracy of critical cardiac structures like LV, RV, and myocardium.
- Addresses the challenge of generalization in cardiac segmentation approaches.
AI-generated from the title and abstract; the full text is not read.
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 ↗
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Field: Health Information Management
Health Information ManagementHealth Professions