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Abstract #3955

Variation-guided supervoxels for subregional tumour analysis in DCE-MRI

Jola Mirecka1, Benjamin Irving1, Danny Allen2, Paul Kinchesh2, Stuart Gilchrist2, Ana Gomes2, Veerle Kersemans2, Sean Smart2, Michael Chappell1, Julia Schnabel3, and Mark Jenkinson4

1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, 2Department of Oncology, University of Oxford, Oxford, United Kingdom, 3School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 4Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

In nature and real life application domains it is common to encounter varied or textured areas, therefore in many cases it is of greater interest to partition the image into similarly varied, as opposed to similarly homogeneous subregions. We propose a novel, variation-guided approach to SLIC clustering, that has a potential to provide a useful alternative to standard supervoxels due to it’s ability to retain local variation information. We evaluate the method on a longitudinal DCE-MRI dataset of 10 mice scanned over 10 days. The method was able to produce contiguous segmentations, while significantly reducing computational complexity.

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