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Cattell, L.* ; Platsch, G.* ; Pfeiffer, R.* ; Declerck, J.* ; Schnabel, J.A.* ; Hutton, C.*

Classification of amyloid status using machine learning with histograms of oriented 3D gradients.

Neuroimage: Clin. 12, 990-1003 (2015)

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	Open Access Gold

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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 133 18F-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 209 11C-PiB images and 128 18F-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.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Amyloid ; Classification ; Florbetaben ; Florbetapir ; Pittsburgh Compound B ; Positron Emission Tomography

e-ISSN 2213-1582

Journal NeuroImage: Clinical

Quellenangaben Volume: 12, Pages: 990-1003

Publisher Elsevier

Publishing Place Amsterdam

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute for Machine Learning in Biomed Imaging (IML)