Clinically oriented automatic 2D liver tumor segmentation: LCMambaNet with a state-space model and liver cancer–specific attention

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.