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

Cattell L, Platsch G, Pfeiffer R, Declerck J, Schnabel JA, Hutton C (2015)

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Publication Type: Journal article

Publication year: 2015

Journal

Book Volume: 12

Pages Range: 990-1003

DOI: 10.1016/j.nicl.2016.05.004

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 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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How to cite

APA:

Cattell, L., Platsch, G., Pfeiffer, R., Declerck, J., Schnabel, J.A., & Hutton, C. (2015). Classification of amyloid status using machine learning with histograms of oriented 3D gradients. NeuroImage: Clinical, 12, 990-1003. https://doi.org/10.1016/j.nicl.2016.05.004

MLA:

Cattell, Liam, et al. "Classification of amyloid status using machine learning with histograms of oriented 3D gradients." NeuroImage: Clinical 12 (2015): 990-1003.

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