Lightweight triangular mesh deformable reconstruction for low quality 3D organ models: Thickness noise and uneven topology

Abstract

Lightweight triangular mesh models have great potential for real-time 3D visualization of lesions during minimally invasive surgery (MIS). However, the blurred tissue boundaries, high imaging noise, and unoriented points in medical images seriously affect the accuracy and topological quality of surface reconstruction, which can lead to inaccurate lesion localization. In this paper, we present a robust and high-topology-quality triangular mesh reconstruction method that aims to provide a deformable expression model for real-time 3D visualization during surgery. Our approach begins by approximating the model prototype under the guidance of an unsigned distance field by simulating inflation. Then, we introduce a variance-controlled cylindrical domain projection search (VC-CDPS) method to achieve the final surface fitting. Additionally, we incorporate topology optimization into the iterative reconstruction process to ensure smoothness and good topology of the reconstruction model. To validate our method, we conduct experiments on a geometric model with high noise and a human organ model manually segmented by novice doctors. The results demonstrate that our reconstructed model exhibits better surface quality and noise immunity. Furthermore, we conduct a comparison experiment of model deformation and propose a metric to measure the topological quality of the model. Through in vitro tissue experiments, we explored the relationship between topological quality and deformation accuracy. The results reveal a positive correlation between deformation accuracy and topological quality.