Intraoperative brain shift estimation using vessel segmentation registration and tracking

Abstract

A typical image-guided neurosurgery system (IGNS) presents images acquired pre-operatively to the surgeons to assist in planning and executing the procedure. The major issue with IGNSs is the fact that the brain shifts during the procedure due to a number of reasons ranging from loss of cerebrospinal fluid, medications, and/or resection of abnormal tissues. Updating the pre-operative images to compensate for brain shift using computational models is an active area of research. In this dissertation, we have developed and evaluated techniques that permit estimating cortical displacements from laser range scanner (LRS) images and intra-operative microscope video sequences. Cortical displacements can be used subsequently as input to computational models. The set of techniques that were developed include novel vessel segmentation algorithms, new registration methods for pre- and post-resection LRS data and new tracking algorithms for microscope video sequences recorded during a tumor resection surgery. Results show that combining LRS images and video sequences is a promising approach to estimate intra-operative brain shift.