Objective:

To propose a unified 2.5D segmentation framework, MemSAM-2.5D, for accurate liver tumor segmentation in 3D CT volumes.

Approach:

• Hybrid Mamba-Adapter (HMA): Integrates intra-slice multi-scale representation to capture contextual correlations.
• Z-axis State Flow (ZSF) module: Models continuous inter-slice dependencies using a one-dimensional state-space model.
• Confidence-Gated Prototype Memory (CGPM): Refines boundaries by evaluating predictive uncertainty and excluding ambiguous regions.

Key Findings:

• MemSAM-2.5D outperforms CNN-based, Transformer-based, Mamba-based, and MedSAM-based baselines on the MSD08, HCC-TACE-Seg, and WAW-TACE datasets.
• Improvements are noted in overlap-based metrics, boundary-sensitive measures, and continuity-related assessments.

Interpretation:

Limitations:

• The study does not address potential overfitting risks associated with the proposed model.
• Further validation on diverse datasets, including those with varying tumor characteristics, may be required to confirm generalizability.

Conclusion:

MemSAM-2.5D provides an effective solution for clinically relevant liver tumor segmentation.