Maximal tumor resection without damaging healthy tissue in open cranial surgeries is critical to the prognosis for patients with brain cancers. Preoperative images (e.g., preoperative magnetic resonance images (pMR)) are typically used for surgical planning as well as for intraoperative image-guidance. However, brain shift even at the start of surgery significantly compromises the accuracy of neuronavigation, if the deformation is not compensated for. Compensating for brain shift during surgical operation is, therefore, critical for improving the accuracy of image-guidance and ultimately, the accuracy of surgery. To this end, we have developed an integrated neurosurgical guidance system that incorporates intraoperative three-dimensional (3D) tracking, acquisition of volumetric true 3D ultrasound (iUS), stereovision (iSV) and computational modeling to efficiently generate model-updated MR image volumes for neurosurgical guidance. The system is implemented with real-time Labview to provide high efficiency in data acquisition as well as with Matlab to offer computational convenience in data processing and development of graphical user interfaces related to computational modeling. In a typical patient case, the patient in the operating room (OR) is first registered to pMR image volume. Sparse displacement data extracted from coregistered intraoperative US and/or stereovision images are employed to guide a computational model that is based on consolidation theory. Computed whole-brain deformation is then used to generate a model-updated MR image volume for subsequent surgical guidance. In this paper, we present the key modular components of our integrated, model-based neurosurgical guidance system.