<![CDATA[Complex liver resections related to hepatic tumors represent a major surgical challenge that requires precise preoperative planning supported by reliable three-dimensional (3D) anatomical models. In this context, accurate volumetric segmentation of the liver is a critical prerequisite to ensure the fidelity of printed models and to optimize surgical decision-making. This study compares different segmentation techniques integrated into open-source software to identify the most suitable approach for clinical application in resource-limited settings. Three semi-automatic methods, region growing, thresholding, and contour interpolation, were tested using the 3D Slicer platform and compared with a proprietary automatic method (Hepatic VCAR, GE Healthcare) and a manual segmentation reference, considered the gold standard. Ten anonymized abdominal CT volumes from the Medical Segmentation Decathlon dataset, encompassing various hepatic pathologies, were used to assess and compare the performance of each technique. Evaluation metrics included the Dice similarity coefficient (Dice), Hausdorff distance (HD), root mean square error (RMS), standard deviation (SD), and colorimetric surface discrepancy maps, enabling both quantitative and qualitative analysis of segmentation accuracy. Among the tested methods, the semi-automatic region growing approach demonstrated the highest agreement with manual segmentation (Dice = 0.935 ± 0.013; HD = 4.32 ± 0.48 mm), surpassing both other semi-automatic techniques and the automatic proprietary method. These results suggest that the region growing method implemented in 3D Slicer offers a reliable, accurate, and reproducible workflow for generating 3D liver models, particularly in surgical environments with limited access to advanced commercial solutions. The proposed methodology can potentially improve surgical planning, enhance training through realistic patient-specific models, and facilitate broader adoption of 3D printing in hepatobiliary surgery worldwide.]]>
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