Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning.
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Authors
Sundaresan, Vaanathi
Arthofer, Christoph
Zamboni, Giovanna
Dineen, Robert A
Rothwell, Peter M
Sotiropoulos, Stamatios N
Auer, Dorothee P
Tozer, Daniel J
Markus, Hugh S
Miller, Karla L
Dragonu, Iulius
Sprigg, Nikola
Alfaro-Almagro, Fidel
Jenkinson, Mark
Griffanti, Ludovica
Publication Date
2021Journal Title
Front Neuroinform
ISSN
1662-5196
Publisher
Frontiers Media SA
Volume
15
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Sundaresan, V., Arthofer, C., Zamboni, G., Dineen, R. A., Rothwell, P. M., Sotiropoulos, S. N., Auer, D. P., et al. (2021). Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning.. Front Neuroinform, 15 https://doi.org/10.3389/fninf.2021.777828
Abstract
Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities.
Keywords
Machine Learning, Structural Mri, Cerebral Microbleeds, Uk Biobank, Susceptibility Weighted Image (Swi), T2*-weighted Mri, Subject-Level Detection
Sponsorship
Medical Research Council (MR/L023784/2)
Identifiers
PMC8811357, 35126079
External DOI: https://doi.org/10.3389/fninf.2021.777828
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334931
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