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