Evaluation of Intra-operative Patient-specific Methods for Point Cloud Completion for Minimally Invasive Liver Interventions
Journal:
arXiv
Published Date:
Mar 15, 2025
Abstract
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.
