Prompt-mamba filtering networks for accurate hepatocellular carcinoma lesion segmentation in abdominal CT

Abstract

Precise delineation of hepatocellular carcinoma (HCC) in abdominal CT is pivotal for early diagnosis and surgical planning, yet remains challenged by morphological heterogeneity, low contrast in small lesions, and scanner variability. To address these limitations, we propose Prompt-Mamba-AF, a framework tailored for robust HCC segmentation. Our method uniquely integrates anatomy-aware prompts to guide feature extraction within liver regions and leverages Mamba-based state-space modeling to capture long-range volumetric dependencies with linear complexity. Furthermore, we introduce structure-aware filtering to enforce topological consistency along lesion boundaries. Extensive validation on the LiTS, 3DIRCADb, and CHAOS benchmarks demonstrates that Prompt-Mamba-AF outperforms current state-of-the-art CNN and Transformer architectures. The model achieves leading Dice similarity and boundary accuracy while maintaining a compact parameter footprint (27.6M). Results indicate significant improvements in small nodule sensitivity and generalization across diverse imaging domains, positioning Prompt-Mamba-AF as an efficient solution for multi-center clinical workflows.