Intracranial vessel segmentation from time-of-flight MRA using pre-processing of the MIP Z-buffer: accuracy of the ZBS algorithm

Abstract

We evaluate the accuracy of a vascular segmentation algorithm which uses continuity in the maximum intensity projection (MIP) depth Z-buffer as a pre-processing step to generate a list of 3D seed points for further segmentation. We refer to the algorithm as Z-buffer segmentation (ZBS). The pre-processing of the MIP Z-buffer is based on smoothness measured using the minimum χ 2 value of a least square fit. Points in the Z-buffer with χ 2 values below a selected threshold are used as seed points for 3D region growing. The ZBS algorithm couples spatial continuity information with intensity information to create a simple yet accurate segmentation algorithm. We examine the dependence of the segmentation on various parameters of the algorithm. Performance is assessed in terms of the inclusion/exclusion of vessel/background voxels in the segmentation of intracranial time-of-flight MRA images. The evaluation is based on 490,256 voxels from 14 patients which were classified by an observer. ZBS performance was compared to simple thresholding and to segmentation based on vessel enhancement filtering. The ZBS segmentation was only weakly dependent on the parameters of the initial MIP image generation, indicating the robustness of this approach. Region growing based on Z-buffer generated seeds was advantageous compared to simple thresholding. The ZBS algorithm provided segmentation accuracies similar to that obtained with the vessel enhancement filter. The ZBS performance was notably better than the filter based segmentation for aneurysms where the assumptions of the filter were violated. As currently implemented the algorithm slightly under-segments the intracranial vasculature.