University of Tasmania
Browse

File(s) under permanent embargo

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

journal contribution
posted on 2023-05-20, 01:05 authored by Aidan O'Mara, Jessica CollinsJessica Collins, Anna KingAnna King, James VickersJames Vickers, Matthew KirkcaldieMatthew Kirkcaldie

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.

History

Publication title

Current Alzheimer Research

Volume

16

Pagination

102-108

ISSN

1567-2050

Department/School

Wicking Dementia Research Education Centre

Publisher

Bentham Science Publishers

Place of publication

United Arab Emirates

Rights statement

© 2018 Bentham Science Publishers

Repository Status

  • Restricted

Socio-economic Objectives

Clinical health not elsewhere classified

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC