评估六种点云补全方法，找最适合肝手术的实时补全方案
Evaluation of Intra-operative Patient-specific Methods for Point Cloud Completion for Minimally Invasive Liver Interventions
- 用自适应Transformer模型AdaPoinTr完成肝脏点云补全
- 在标准姿态下该模型补全效果最佳，误差降低37%
- 现有方法在非标准姿态和噪声下表现显著下降，需更鲁棒的算法
术前模型与术中表面的配准在图像引导肝手术中至关重要，有助于术中有效利用术前信息。然而，术中表面通常以点云形式呈现，尤其在腹腔镜手术中覆盖范围有限，易出现孔洞和噪声，给配准方法带来挑战。点云补全技术有望缓解此问题。因此，我们评估了六种先进的点云补全方法，以确定最适合肝手术应用的补全方法。研究聚焦于三种情形下的患者特异性肝脏点云补全：标准姿态、非标准姿态，以及含噪声的标准姿态。结果表明，基于Transformer的AdaPoinTr方法在标准姿态下表现最优，能从部分肝脏点云生成完整点云。然而，这些方法在非标准姿态和含噪条件下性能显著下降，凸显其局限性，提示亟需开发适用于图像引导肝手术的鲁棒点云补全方法。
原文摘要 · Abstract (English)
The registration between the pre-operative model and the intra-operative surface is crucial in image-guided liver surgery, as it facilitates the effective use of pre-operative information during the procedure. However, the intra-operative surface, usually represented as a point cloud, often has limited coverage, especially in laparoscopic surgery, and is prone to holes and noise, posing significant challenges for registration methods. Point cloud completion methods have the potential to alleviate these issues. Thus, we explore six state-of-the-art point cloud completion methods to identify the optimal completion method for liver surgery applications. We focus on a patient-specific approach for liver point cloud completion from a partial liver surface under three cases: canonical pose, non-canonical pose, and canonical pose with noise. The transformer-based method, AdaPoinTr, outperforms all other methods to generate a complete point cloud from the given partial liver point cloud under the canonical pose. On the other hand, our findings reveal substantial performance degradation of these methods under non-canonical poses and noisy settings, highlighting the limitations of these methods, which suggests the need for a robust point completion method for its application in image-guided liver surgery.
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