White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks

Guerrero, R. et al. (2018) White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. NeuroImage: Clinical, 17, pp. 918-934. (doi: 10.1016/j.nicl.2017.12.022) (PMID:29527496) (PMCID:PMC5842732)

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Abstract

White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes.

Item Type:Articles
Additional Information:The research presented here was partially funded by Innovate UK (formerly UK Technology Strategy Board) grant no. 102167 and by the 7th Framework Programme by the European Commission (http://cordis.europa.eu; EU-grant-611005-PredictND – From Patient Data to Clinical Diagnosis in Neurodegenerative Diseases). Additionally, data used in preparation of this work was obtained under funding by the Row Fogo Charitable Trust (AD.ROW4.35. BRO-D.FID3668413) for MVH, and the Wellcome Trust (WT088134/Z/09/A).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Dickie, Dr David Alexander
Authors: Guerrero, R., Qin, C., Oktay, O., Bowles, C., Chen, L., Joules, R., Wolz, R., Valdés-Hernández, M.C., Dickie, D.A., Wardlaw, J., and Rueckert, D.
College/School:College of Medical Veterinary and Life Sciences > Institute of Cardiovascular and Medical Sciences
Journal Name:NeuroImage: Clinical
Publisher:Elsevier
ISSN:2213-1582
ISSN (Online):2213-1582
Published Online:20 December 2017
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in NeuroImage: Clinical 17:918-934
Publisher Policy:Reproduced under a Creative Commons License

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