The accuracy of the 3D models created from medical scans depends on imaging hardware, segmentation methods and mesh processing techniques etc. The effects of geometry type, class imbalance, voxel and point cloud alignment on accuracy remain to be thoroughly explored. This work evaluates the errors across the reconstruction pipeline and explores the use of voxel and surface-based accuracy metrics for different segmentation algorithms and geometry types. A sphere, a facemask, and an AAA were printed using the SLA technique and scanned using a micro-CT machine. Segmentation was performed using GMM, Otsu and RG based methods. Segmented and reference models aligned using the KU algorithm, were quantitatively compared to evaluate metrics like Dice and Jaccard scores, precision. Surface meshes were registered with reference meshes using an ICP-based alignment process. Metrics like chamfer distance, and average Hausdorff distance were evaluated. The Otsu method was found to be the most suitable method for all the geometries. AAA yielded low overlap scores due to its small wall thickness and misalignment. The effect of class imbalance on specificity was observed the most for AAA. Surface-based accuracy metrics differed from the voxel-based trends. The RG method performed best for sphere, while GMM and Otsu perform better for AAA. The facemask surface was most error-prone, possibly due to misalignment during the ICP process. Segmentation accuracy is a cumulative sum of errors across different stages of the reconstruction process. High voxel-based accuracy metrics may be misleading in cases of high class imbalance and sensitivity to alignment. The Jaccard index is found to be more stringent than the Dice and more suitable for accuracy assessment for thin-walled structures. Voxel and point cloud alignment should be ensured to make any reliable assessment of the reconstruction pipeline.

翻译：从医学扫描数据创建三维模型的精度取决于成像硬件、分割方法及网格处理技术等因素。几何类型、类别不平衡、体素与点云配准对精度的影响尚待深入探究。本研究评估了重建流程中的误差，并探讨了针对不同分割算法与几何类型采用体素与表面精度指标的适用性。通过立体光刻技术打印球体、面罩及腹主动脉瘤模型，并采用微CT设备进行扫描。使用基于高斯混合模型、大津法与区域生长法的分割方法进行处理。通过KU算法对分割模型与参考模型进行配准后，定量比较戴斯系数、杰卡德指数及精确率等指标。采用基于迭代最近点的配准流程实现表面网格与参考网格的对齐，评估倒角距离与平均豪斯多夫距离等指标。研究发现大津法对所有几何形状均最为适用。腹主动脉瘤模型因壁厚较薄与配准偏差导致重叠评分较低。类别不平衡对特异性的影响在腹主动脉瘤模型中最为显著。表面精度指标与体素精度指标呈现不同变化趋势：区域生长法在球体分割中表现最优，而高斯混合模型与大津法在腹主动脉瘤分割中精度更高。面罩模型的表面误差最为显著，可能源于迭代最近点配准过程中的未对准现象。分割精度是重建流程各阶段误差的累积结果。在严重类别不平衡及配准敏感场景中，高体素精度指标可能产生误导。研究发现杰卡德指数较戴斯系数更为严格，更适用于薄壁结构的精度评估。为确保重建流程评估的可靠性，必须保证体素与点云数据的精确配准。