title: Classification of amyloid status using machine learning with histograms of oriented 3D gradients.
abstract: Brain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, however, the amyloid positivity threshold is dependent on the tracer and specific image regions used to calculate the uptake ratio. To address this problem, we propose a machine learning approach to amyloid status classification, which is independent of tracer and does not require a specific set of regions of interest. Our method extracts feature vectors from amyloid images, which are based on histograms of oriented three-dimensional gradients. We optimised our method on 13318F-florbetapir brain volumes, and applied it to a separate test set of 131 volumes. Using the same parameter settings, we then applied our method to 20911C-PiB images and 12818F-florbetaben images. We compared our method to classification results achieved using two other methods: standardised uptake value ratios and a machine learning method based on voxel intensities. Our method resulted in the largest mean distances between the subjects and the classification boundary, suggesting that it is less likely to make low-confidence classification decisions. Moreover, our method obtained the highest classification accuracy for all three tracers, and consistently achieved above 96% accuracy.
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK.
authors.forename: Liam
authors.initials: L
authors.lastname: Cattell
authors.affiliation: Siemens Molecular Imaging, Oxford, UK.
authors.forename: Günther
authors.initials: G
authors.lastname: Platsch
authors.affiliation: Piramal Imaging, Berlin, Germany.
authors.forename: Richie
authors.initials: R
authors.lastname: Pfeiffer
authors.affiliation: Siemens Molecular Imaging, Oxford, UK.
authors.forename: Jérôme
authors.initials: J
authors.lastname: Declerck
authors.affiliation: Division of Imaging Sciences and Biomedical Engineering, King's College London, UK.
authors.forename: Julia A
authors.initials: JA
authors.lastname: Schnabel
authors.affiliation: Siemens Molecular Imaging, Oxford, UK.
authors.forename: Chloe
authors.initials: C
authors.lastname: Hutton
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000145:classification / Q000000981:diagnostic imaging* / Q000378:metabolism*; D000682:Amyloid / Q000378:metabolism*; D000814:Aniline Compounds; D001921:Brain / Q000000981:diagnostic imaging / Q000378:metabolism*; D002250:Carbon Radioisotopes; D005026:Ethylene Glycols; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013267:Stilbenes; D060388:Support Vector Machine
chemical_list: D000682:Amyloid; D000814:Aniline Compounds; D002250:Carbon Radioisotopes; D005026:Ethylene Glycols; D019275:Radiopharmaceuticals; D013267:Stilbenes; C545186:florbetapir; C527756:4-(N-methylamino)-4'-(2-(2-(2-fluoroethoxy)ethoxy)ethoxy)stilbene
keywords: Amyloid; Classification; Florbetaben; Florbetapir; Pittsburgh compound B; Positron emission tomography