MemSAM-2.5D: overcoming volumetric discontinuity and boundary ambiguity for 3D liver tumor segmentation.

Introduction: Accurate segmentation of liver tumors from 3D computed tomography (CT) volumes is essential for the clinical management of hepatocellular carcinoma (HCC), but remains challenging because of extreme lesion-scale variation, volumetric discontinuity across slices, and ambiguous tumor boundaries. Methods: We propose MemSAM-2.5D, a unified 2.5D segmentation framework built upon the MedSAM foundation model. The framework integrates a Hybrid Mamba-Adapter (HMA) for intra-slice multi-scale representation, a Z-axis State Flow (ZSF) module for continuous inter-slice dependency modeling, and a Confidence-Gated Prototype Memory (CGPM) module for uncertainty-aware boundary refinement. Results: Extensive evaluations on MSD08, HCC-TACE-Seg, and WAW-TACE demonstrate that MemSAM-2.5D consistently outperforms representative CNN-based, Transformer-based, Mamba-based, and MedSAM-based baselines. The improvements are reflected not only in overlap-based metrics, but also in boundary-sensitive, lesion-level, and continuity-related measures. Discussion: These results suggest that coordinated modeling of multi-scale lesion variability, z-axis continuity, and boundary ambiguity provides an effective and transferable solution for clinically relevant HCC segmentation in CT volumes.