Point cloud registration for measuring shape dependence of soft tissue deformation by digital twins in head and neck surgery

Introduction: A 2D point cloud registration method was developed to generate digital twins of different tissue shapes and resection cavities by applying a machine learning (ML) approach. This demonstrates the feasibility of quantifying soft tissue shifts. Methods: An ML model was trained using simulated surface scan data obtained from tumor resections in a pig head cadaver model. It hereby uses 438 2D scans of the tissue surfaces. tissue shift was induced by a temperature change from 7,91 ± 4,1°C to 36,37 ± 1,28 °C. Results: Digital twins were generated from various branched and compact resection cavities (RC) and cut tissues (CT). Temperature increase induced a tissue shift with a significant volume increase of 6 ml and 2 ml in branched and compact RC, respectively (p=0.0443; 0.0157). The volumes of branched and compact CT decreased by 3 and 4 ml (p Conclusions: The simulation experiment of induced soft tissue deformation using digital twins based on 2point cloud models proved that our method helps to quantify shape-dependent tissue shifts.