METHOD AND MATERIALS: A pattern recognition approach based on global and local image features followed by a random forest classifier is proposed. The features consist of an automatically computed brain mask denoting intracranial tissue, the first volume of the CTP scan, the CTP scan temporal average weighted according to the individual exposures to maximize signal-to-noise ratio, the weighted temporal variance (WTV), and local histogram features of the WTV calculated in a neighborhood of 9x9x9 voxels around a centered voxel. The mean, standard deviation, entropy and mode of the histogram are extracted as local feature values. In total 26 patients that underwent CTP for suspicion of stroke were included in this study. The CTP was acquired on a 320-detector row scanner. Image size was 512x512x320 voxels by 19 time points with voxel sizes of approximately 0.5 mm. Training was done on 8 patients with manually annotated data. The remaining 18 patients were used as testing set. Segmentations were visually inspected for completeness and overall quality. 3D-patches including the M2/M3 segments of the middle cerebral artery were manually annotated for quantitative evaluation. The modified Hausdorff distance (MHD) (maximum of the median HDs) and the accuracy (true positive + true negative voxels divided by amount of voxels in a patch) of the segmentation were reported for the annotated patches.
RESULTS: Overall the method was capable of segmenting the complete cerebral vasculature with inclusion of very small distal vessels. Parts of one internal carotid was missed in one patient because of clipping artefacts. In 3 patients false positive voxels were observed in the skull base region near the internal carotid artery and cavernous sinus. The MHD was 0.51Ã¯Â¿Â½0.28 mm, which is similar to the voxel spacing, and the accuracy was 0.97Ã¯Â¿Â½0.01.
CONCLUSION: Our approach provides high-quality segmentation of small cerebral vessels from CTP data.CLINICAL RELEVANCE/APPLICATION: The high quality segmentation provided by our approach is an important step towards the automated localization and evaluation of vascular pathology in acute stroke patients.
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