Clinically oriented automatic 2D liver tumor segmentation: LCMambaNet with a state-space model and liver...

Liver cancer is among the deadliest malignancies worldwide, and both its incidence and mortality continue to rise. Precise tumor segmentation often remains difficult due to heterogeneous enhancement patterns, infiltrative margins, and frequently obscured underlying parenchymal disease. While deep learning has advanced the field, existing heavy 3D architectures (e.g., nnU-Net) often require substantial computational resources, which limits their clinical deployment. Standard architectures also still struggle to reconcile fine-grained tissue cues with whole-organ context.

This study introduces the Liver Cancer Mamba Network (LCMambaNet), an efficient 2D segmentation framework built on selective state-space models. A tailored scan-patch mechanism extracts salient texture- and density-based features, sharpening the discrimination between normal parenchyma and malignant regions. The Liver Cancer Attention Module (LCAM) further decouples the confounding relationships between parenchymal descriptors and tumor characteristics. The selective state-space backbone captures long-range dependencies and continuous feature dynamics. We evaluated the model on both the LITS (CT) and CirrMR160+ (MRI) datasets.

The proposed approach surpasses current state-of-the-art methods, achieving Dice scores of 92.94 ± 3.12% and 92.08 ± 2.85% on the LITS and CirrMR160+ datasets, respectively. Notably, stratified analysis shows superior performance on small lesions (< 2 cm), with statistical significance (p < 0.01) against strong baseline models. Comprehensive ablation studies verify the contribution of each component.

The results demonstrate that LCMambaNet offers an efficient, clinically viable solution for 2D liver tumor segmentation. Its design addresses the key limitations of existing models, balancing computational efficiency with high segmentation accuracy. The strong performance on small lesions also highlights its potential to support early diagnosis and precise treatment planning, advancing the clinical utility of AI-based segmentation tools.