Biomechanical models that describe soft-tissue deformations provide a relatively inexpensive way to correct registration
errors in image guided neurosurgical systems caused by non-rigid brain shifts. Quantifying the factors that cause this
deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based method have been
developed recently which allow for uncertainty yet still capture the first order effects associated with brain deformations.
More specifically, the technique involves building an atlas of solutions to account for the statistical uncertainty in factors
that control the direction and magnitude of brain shift. The inverse solution is driven by a sparse intraoperative surface
measurement. Since this subset of data only provides surface information, it could bias the reconstruction and affect the
subsurface accuracy of the model prediction. Studies in intraoperative MR have shown that the deformation in the
midline, tentorium, and contralateral hemisphere is relatively small. The falx cerebri and tentorium cerebelli, two of the
important dural septa, act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain.
Accounting for these structures in models may be an important key to improving subsurface shift accuracy. The goals of
this paper are to describe a novel method developed to segment the tentorium cerebelli, develop the procedure for
modeling the dural septa and study the effect of those membranes on subsurface brain shift.
Measurement of intra-operative cortical brain movement is necessary to drive mechanical models developed to predict
sub-cortical shift. At our institution, this is done with a tracked laser range scanner. This device acquires both 3D range
data and 2D photographic images. 3D cortical brain movement can be estimated if 2D photographic images acquired
over time can be registered. Previously, we have developed a method, which permits this registration using vessels
visible in the images. But, vessel segmentation required the localization of starting and ending points for each vessel
segment. Here, we propose a method, which automates the segmentation process further. This method involves several
steps: (1) correction of lighting artifacts, (2) vessel enhancement, and (3) vessels' centerline extraction. Result obtained
on 5 images obtained in the operating room suggests that our method is robust and is able to segment vessels reliably.