Nonrigid Object Modelling and Visualization for Hepatic Surgery Planning in e-Health
This paper introduces an automatic approach of nonrigid object modelling and visualization for hepatic surgery planning, in particular, for live donor liver transplantation and accurate liver resection for cancer in e-health application. The proposed approach can build a system that supports radiologists in data preparation and gives surgeons precise information for making optimal decisions. It provides 3D representation of liver parenchyma and vasculature, and 3D simulation of patient specific data. The system is realized in four major stages, including registration of multimodal images; segmentation of liver parenchyma; extraction of liver vessels; and modelling and visualization of liver parenchyma and vessels. The approach is unique in that it integrates advanced techniques such as machine learning algorithm with a knowledge base of the organ. The details of these stages are described along with experimental results and discussions of the advantages of the approach over other approaches.
Keywordsvisualization modeling segmentation machine learning surgery planning
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