Tumors exhibit chaotic and leaky vasculature, which leads to variations in perfusion, and regions of edema, hypoxia and necrosis. We develop a method to extract perfusion-supervoxels, regions of locally similar perfusion, and use these regions with k-means clustering to define tumor subregions that are robust to noise and outliers. This method offers a number of advantages over both mean tumor parameters and voxelwise clustering.
This abstract and the presentation materials are available to members only; a login is required.