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Abstract

Background: Images of amyloid-β pathology characteristic of Alzheimer’s disease are difficult to consistently andaccurately segment, due to diffuse deposit boundaries and imaging variations.Methods: We evaluated the performance of ImageSURF, our open-source ImageJ plugin, which considers a range of imagederivatives to train image classifiers. We compared ImageSURF to standard image thresholding to assess its reproducibility,accuracy and generalizability when used on fluorescence images of amyloid pathology.Results: ImageSURF segments amyloid-β images significantly more faithfully, and with significantly greatergeneralizability, than optimized thresholding.Conclusion: In addition to its superior performance in capturing human evaluations of pathology images, ImageSURF isable to segment image sets of any size in a consistent and unbiased manner, without requiring additional blinding, and canbe retrospectively applied to existing images. The training process yields a classifier file which can be shared assupplemental data, allowing fully open methods and data, and enabling more direct comparisons between different studies.

Item Type:	Article
Authors/Creators:	O'Mara, AR and Collins, JM and King, AE and Vickers, JC and Kirkcaldie, MTK
Keywords:	amyloid, Alzheimer's disease, segmentation, machine learning
Journal or Publication Title:	Current Alzheimer Research
Publisher:	Bentham Science Publishers
ISSN:	1567-2050
DOI / ID Number:	10.2174/1567205016666181212152622
Copyright Information:	© 2018 Bentham Science Publishers
Related URLs:	UTAS Profile: King, AE UTAS Profile: Vickers, JC Google Scholar: Kirkcaldie, MTK UTAS Profile: Kirkcaldie, MTK
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