Hierarchical Mamba Networks for Multi-Organ Segmentation in Abdominal Medical Imaging

Abstract

Abdominal multi-organ segmentation remains a critical task in medical image analysis because quantitative organ delineation supports treatment planning, surgical guidance, and disease monitoring. Although deep learning has produced substantial advances, convolutional approaches tend to emphasize local spatial patterns, while transformer-based methods can capture long-range context at considerable computational cost. Hierarchical Mamba networks represent an emerging alternative that combines selective state space sequence modeling with multiscale representation learning. This paper provides a system-level analysis of hierarchical Mamba networks for multi-organ segmentation in abdominal imaging. Rather than focusing solely on architectural novelty, the discussion addresses structural trade-offs in representation capacity, memory use, scan ordering, and multiscale fusion. It further examines data infrastructure and governance requirements, including annotation quality, demographic bias, privacy protections, and federated learning arrangements. Deployment concerns such as prospective validation, input verification, uncertainty visualization, and integration with clinical workflows are explored. Fairness, regulatory accountability, and sustainability are treated as first-order design constraints rather than post hoc considerations. The paper argues that hierarchical Mamba architectures can support more efficient long-range modeling than attention-based alternatives, but their clinical value depends on robust governance, operational monitoring, and deliberate alignment with diverse patient populations. Future directions include adaptive scan strategies, uncertainty-aware abstention, longitudinal cross-fusion, and resource-aware deployment for lower-resource settings.