通过微CT幻影实验，揭示几何形状与类别不平衡对3D重建精度的影响。
Influence of Geometry, Class Imbalance and Alignment on Reconstruction Accuracy -- A Micro-CT Phantom-Based Evaluation
- 用球体、面罩和动脉瘤模型测试不同分割方法的误差积累
- Otsu法整体表现最佳，但薄壁结构因对齐敏感导致重叠率低
- 表面度量指标与体素指标趋势不一，需结合使用评估精度
3D医学模型重建精度受成像设备、分割算法和网格处理等多重因素影响。本文系统评估了几何类型、类别不平衡、体素与点云对齐等因素对重建误差的影响。采用SLA技术打印球体、面罩和主动脉瘤（AAA）模型，并通过微CT扫描获取数据。使用GMM、Otsu和RG方法进行分割，通过KU算法对齐分割结果与参考模型，计算Dice、Jaccard、精度等指标。表面网格则通过基于ICP的配准方式与参考网格对比，评估切比雪夫距离和平均豪斯多夫距离。结果显示，Otsu方法在所有几何中表现最优；由于壁厚小且存在对齐偏差，AAA的重叠得分最低；类别不平衡对AAA的特异性影响最显著。表面度量指标与体素度量趋势不一致。对于球体，RG方法最优；对于AAA，GMM与Otsu更优。面罩表面误差最大，可能源于ICP过程中的对齐问题。分割精度是重建全流程误差的累积结果。高体素精度在类别不平衡或对齐敏感场景下可能具有误导性。研究发现Jaccard指数比Dice更严格，更适合薄壁结构评估。必须确保体素与点云对齐，才能可靠评估重建流程性能。
原文摘要 · Abstract (English)
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.
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