Repository logo
 

Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Sundaresan, Vaanathi 
Arthofer, Christoph 
Zamboni, Giovanna 
Dineen, Robert A 
Rothwell, Peter M 

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.

Description

Keywords

T2*-weighted MRI, UK Biobank, cerebral microbleeds, machine learning, structural MRI, subject-level detection, susceptibility weighted image (SWI)

Journal Title

Front Neuroinform

Conference Name

Journal ISSN

1662-5196
1662-5196

Volume Title

15

Publisher

Frontiers Media SA
Sponsorship
Medical Research Council (MR/L023784/2)