Cortical surface registration using texture mapped point clouds and mutual information

Abstract. An inter-modality registration algorithm that uses textured point clouds and mutual information is presented within the context of a new physical-space to image-space registration technique for imageguided neurosurgery. The approach uses a laser range scanner that acquires textured geometric data of the brain surface intraoperatively and registers the data to grayscale encoded surfaces of the brain extracted from gadolinium enhanced MR tomograms. Intra-modality as well as inter-modality registration simulations are presented to evaluate the new framework. The results demonstrate alignment accuracies on the order of the resolution of the scanned surfaces (i.e. submillimetric). In addition, data are presented from laser scanning a brain’s surface during surgery. The results reported support this approach as a new means for registration and tracking of the brain surface during surgery.

A topologically faithful, tissue-guided, spatially varying meshing strategy for computing patient-specific head models for endoscopic pituitary surgery simulation

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A topologically faithful, tissue-guided, spatially varying meshing strategy for computing patient-specific head models for endoscopic pituitary surgery simulation

International audienceno abstrac

Tractable and Reliable Registration of 2D Point Sets

This paper introduces two new methods of registering 2D point sets over rigid transformations when the registration error is based on a robust loss function. In contrast to previous work, our methods are guaranteed to compute the optimal transformation, and at the same time, the worst-case running times are bounded by a low-degree polynomial in the number of correspondences. In practical terms, this means that there is no need to resort to ad-hoc procedures such as random sampling or local descent methods that cannot guarantee the quality of their solutions. We have tested the methods in several different settings, in particular, a thorough evaluation on two benchmarks of microscopic images used for histologic analysis of prostate cancer has been performed. Compared to the state-of-the-art, our results show that the methods are both tractable and reliable despite the presence of a significant amount of outliers