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Accurate and unbiased quantitation of Amyloid-β fluorescence images using ImageSURF




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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 |
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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 |
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Item Statistics: | View statistics for this item |
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