MemSAM-2.5D: overcoming volumetric discontinuity and boundary ambiguity for 3D liver tumor segmentation
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By
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Yinyin Hou
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Ningning Chen
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Tingting Huo
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Weijia Wang
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July 8, 2026
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Clinical Scorecard: MemSAM-2.5D: Addressing Volumetric Discontinuities and Boundary Uncertainty in 3D Segmentation of Liver Tumors
At a Glance
| Category | Detail |
|---|
| Condition | Hepatocellular carcinoma (HCC) |
| Key Mechanisms | Integration of multi-scale representation, inter-slice dependency modeling, and uncertainty-aware boundary refinement. |
| Target Population | Patients with liver tumors requiring accurate segmentation for clinical management. |
| Care Setting | Clinical diagnosis, treatment planning, and prognostic evaluation. |
Key Highlights
- MemSAM-2.5D outperforms existing CNN-based and Transformer-based segmentation methods.
- Utilizes a Hybrid Mamba-Adapter for enhanced intra-slice feature representation.
- Incorporates a Z-axis State Flow module for continuous inter-slice modeling.
- Employs a Confidence-Gated Prototype Memory for robust boundary refinement.
- Demonstrated effectiveness on general liver tumor benchmarks and HCC-specific datasets.
Guideline-Based Recommendations
Diagnosis
- Accurate segmentation of liver tumors is essential for clinical management.
Management
- Utilize advanced segmentation frameworks like MemSAM-2.5D for improved tumor delineation.
Monitoring & Follow-up
- Regular evaluation of segmentation accuracy and boundary delineation in clinical practice.
Risks
- Ambiguous tumor boundaries can lead to false positives in diagnosis.
Patient & Prescribing Data
Patients diagnosed with hepatocellular carcinoma.
Accurate segmentation aids in treatment planning and prognostic evaluation.
Clinical Best Practices
- Implement advanced deep learning algorithms for tumor segmentation.
- Consider multi-scale and volumetric continuity in segmentation approaches.
- Evaluate uncertainty in boundary predictions to enhance diagnostic accuracy.
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