Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data

Please always quote using this URN: urn:nbn:de:0297-zib-65339
  • In this study we investigate methods for fitting a Statistical Shape Model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system. We validate the fitted models against the pre-operatively acquired Magnetic Resonance Imaging (MRI) data from the same patients. We consider a cohort of 10 patients who underwent navigated total knee arthroplasty. As part of the surgical protocol the patients’ distal femurs were partially digitized. All patients had an MRI scan two months pre-operatively. The MRI data were manually segmented and the reconstructed bone surfaces used as ground truth against which the fit was compared. Two methods were used to fit the SSM to the data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM). For both approaches, the difference between model fit and ground truth surface averaged less than 1.7 mm and excellent correspondence with the distal femoral morphology can be demonstrated.
Metadaten
Author:Felix Ambellan, Alexander Tack, Dave Wilson, Carolyn Anglin, Hans Lamecker, Stefan Zachow
Document Type:In Proceedings
Parent Title (English):Proceedings of the Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC)
Volume:16
First Page:24
Last Page:30
Tag:Sparse Geometry Reconstruction; Statistical Shape Models; Total Knee Arthoplasty
Year of first publication:2017
Preprint:urn:nbn:de:0297-zib-66052