Benjamin John Irving 1, Jolanta Mirecka1, Ana L Gomes2, Danny Allen2, Paul Kinchesh2, Veerle Kersemans2, Stuart Gilchrist2, Sean Smart2, Julia A Schnabel3, Sir J Michael Brady2, and Michael Chappell1
1Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom, 2Department of Oncology, University of Oxford, Oxford, United Kingdom, 3Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
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.