Influencing Factors on the Registration Accuracy of a Learned Feature Descriptor in Laparoscopic Liver Surgery

Abstract In laparoscopic liver surgery, image-guided navigation systems provide crucial support to surgeons by supplying information about tumor and vessel positions. For this purpose, these information from a preoperative CT or MRI scan is overlaid onto the laparoscopic video. One option is performing a registration of preoperative 3D data and 3D reconstructed laparoscopic data. A robust registration is challenging due to factors like limited field of view, liver deformations, and 3D reconstruction errors. Since in reality various influencing factors always intertwine, it is crucial to analyze their combined effects. This paper assesses registration accuracy under various synthetically simulated influences: patch size, spatial displacement, Gaussian deformations, holes, and downsampling. The objective is to provide insights into the required quality of the intraoperative 3D surface patches. LiverMatch serves as the feature descriptor, and registration employs the RANSAC algorithm. The results of this paper show that ensuring a large field of view of at least 15-20% of the liver surface is necessary, allowing tolerance for less accurate depth estimation.