Real-Time Image Segmentation for Image-Guided Surgery

Image-guided surgery is an application for which high performance computing is increasingly becoming a critical technology. Advances in image-guided surgery techniques have made it possible to acquire images of a patient whilst the surgery is taking place, to align these images with high resolution 3D scans of the patient acquired preoperatively and to merge intraoperative images from multiple imaging modalities. The application of these technologies has now become a routine clinical procedure in some hospitals. However, as the type of procedures undertaken is expanded, it is becoming clear that the use of image fusion and linear registration technology alone has some limitations. We have developed a novel image segmentation algorithm that makes use of an individualized template of normal patient anatomy in order to compute the segmentation of intraoperative imaging data. Intraoperative image segmentation is highly data and compute intensive. In order to achieve accurate segmentation in a time frame compatible with surgical intervention, we have developed a parallel version of our segmentation algorithm, and implemented the algorithm on a symmetric multiprocessor architecture. We have studied the accuracy of the segmentation algorithm, and the scalability and bandwidth requirements of our parallel implementation.