Evaluation of a New 3D/2D Registration Criterion for Liver Radio-Frequencies Guided by Augmented Reality

Abstract

International audienceno abstrac

Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements

“…The interested reader will find practical applications in computer vision to compute the mean rotation [32,33] or for the generalization of matching algorithms to arbitrary geometric features [65]. In medical image analysis, selected applications cover the validation of the rigid registration accuracy [4,66,67], shape statistics [7] and more recently tensor computing, either for processing and analyzing diffusion tensor images [10,8,9,11], or to model the brain variability [12]. One can even find applications in rock mechanics with the analysis of fracture geometry [68].…”

Bootstrap Resampling for Image Registration Uncertainty Estimation Without Ground Truth

“…More realistic but less widely applicable 'gold standard' approach is to use some independent and sufficiently accurate method to determine the true deformation, such as using special markers for validation which are not used for registration [23]- [25]. A "bronze standard" [26], [27] uses a robust mean of several registration algorithms as a reference. The registration accuracy can also be estimated indirectly, from ground truth segmentations [28], [29] or by its ability to create good generative models [30].…”

Virtual Reality and Augmented Reality in Digestive Surgery

“…In this context, 3D points are spatial coordinates of fiducials reconstructed from data stemming from the scanner, and 2D points are pixel coordinates corresponding to fiducials in both camera views. The experimental study, carried out with an abdominal phantom showed that superimposition accuracy for targets located inside the model reaches an average lower than 2 mm after fiducial registration [15]. …”

“…These fiducials, visible in both CT-scan and video images, are automatically extracted and matched thanks to several robust algorithms that were validated in [14]. In order to carry out this registration, we orient two tri-CDD digital color cameras jointly calibrated toward the patient with an angle above 20 for an accurate stereoscopic registration [15]. These cameras are connected to a personal computer thanks to a Matrox Meteor II acquisition card allowing the simultaneous acquisition of two video sources.…”

“…However, this criterion abusively supposes that the noise corrupting the 3D data is negligible. Since we need the best possible accuracy, we use instead the Extended Projective Points Criterion (EPPC) that we developed in [15], and that is optimal in the case of noise on both 2D and 3D markers extraction. In this context, 3D points are spatial coordinates of fiducials reconstructed from data stemming from the scanner, and 2D points are pixel coordinates corresponding to fiducials in both camera views.…”