ArticleInternational journal of computer assisted radiology and surgery2026
AMR-VSF: an adaptive memory correction framework for robust 3D CT aorta segmentation.
Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeAccurate 3D aortic segmentation in CT images is vital for cardiovascular disease diagnosis, surgical planning, and intraoperative navigation. Existing aortic segmentation methods suffer from 3D incoherence (frame-level models) or cumulative memory drift (memory-based video models), failing to meet real-time clinical demands for surgical planning. This study developed the Adaptive Memory Rectification Video Segmentation Framework (AMR-VSF) to integrate both models' strengths, optimizing single-frame anatomical accuracy and cross-frame temporal stability while meeting real-time demands.
methodsAMR-VSF uses a 4-stage modular architecture: (1) Frame-level models generate anatomically complete initial masks; (2) MedSAM2 enables cross-frame mask propagation for temporal consistency; (3) Dual "coverage-diameter deviation rate" metrics detect drift, with segmented memory reset (partial clearing for stable areas, full clearing for complex ones) to correct it; (4) Dynamic weight fusion balances local accuracy and global consistency. TensorRT optimized MedSAM2, and the framework was validated on 92 patients' CT aortic data (≈76,000 slices).
resultsAMR-VSF (nnUNet-based) achieved optimal performance: Dice Similarity Coefficient (DSC) 0.963 (5.1% higher than MedSAM2 alone), 95% Hausdorff Distance (HD95) 3.32 mm (38.2% lower), and Average Surface Distance (ASD) 0.61 mm (38.4% lower than the latter). TensorRT cut MedSAM2's single-frame inference time from 73.1 to 31.2 ms with no accuracy loss. Its outputs enabled 3D reconstruction of the aorta (complete "aortic arch-thoracic aorta-abdominal aorta-branch vessels" topological connection, anatomy-matched smooth edges), and the framework showed strong compatibility with diverse models, with potential extension to other long-sequence luminal structure segmentation.
conclusionAMR-VSF addresses existing limitations by combining frame-level and video models, achieving high accuracy and stability. TensorRT enables real-time use, and its 3D models support tasks like aneurysm measurement. It is compatible and extensible, supporting precise cardiovascular care.
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