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

Citation

O'Mara, AR and Collins, JM and King, AE and Vickers, JC and Kirkcaldie, MTK, Accurate and unbiased quantitation of Amyloid-β fluorescence images using ImageSURF, Current Alzheimer Research, 16, (2) pp. 102-108. ISSN 1567-2050 (2018) [Refereed Article]

Copyright Statement

© 2018 Bentham Science Publishers

DOI: doi:10.2174/1567205016666181212152622

Abstract

Background: Images of amyloid-β pathology characteristic of Alzheimer’s disease are difficult to consistently and accurately 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 image derivatives 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 greater generalizability, than optimized thresholding.

Conclusion: In addition to its superior performance in capturing human evaluations of pathology images, ImageSURF is able to segment image sets of any size in a consistent and unbiased manner, without requiring additional blinding, and can be retrospectively applied to existing images. The training process yields a classifier file which can be shared as supplemental data, allowing fully open methods and data, and enabling more direct comparisons between different studies.

Item Details

Item Type:Refereed Article
Keywords:amyloid, Alzheimer's disease, segmentation, machine learning
Research Division:Medical and Health Sciences
Research Group:Neurosciences
Research Field:Neurology and Neuromuscular Diseases
Objective Division:Health
Objective Group:Clinical Health (Organs, Diseases and Abnormal Conditions)
Objective Field:Neurodegenerative Disorders Related to Ageing
UTAS Author:O'Mara, AR (Mr Aidan O'Mara)
UTAS Author:Collins, JM (Dr Jessica Collins)
UTAS Author:King, AE (Associate Professor Anna King)
UTAS Author:Vickers, JC (Professor James Vickers)
UTAS Author:Kirkcaldie, MTK (Dr Matthew Kirkcaldie)
ID Code:130933
Year Published:2018
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2019-02-20
Last Modified:2019-04-15
Downloads:0

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