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Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage

Pszczolkowski, Stefan; Law, Zhe K.; Gallagher, Rebecca G.; Meng, Dewen; Swienton, David J.; Morgan, Paul S.; Bath, Philip M.; Sprigg, Nikola; Dineen, Rob A.

Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage Thumbnail


Authors

Zhe K. Law

Rebecca G. Gallagher

Dewen Meng

David J. Swienton

Paul S. Morgan

PHILIP BATH philip.bath@nottingham.ac.uk
Stroke Association Professor of Stroke Medicine

NIKOLA SPRIGG nikola.sprigg@nottingham.ac.uk
Professor of Stroke Medicine

ROBERT DINEEN rob.dineen@nottingham.ac.uk
Professor of Neuroradiology



Abstract

Background
Spontaneous intracerebral haemorrhage (SICH) is a common condition with high morbidity and mortality. Segmentation of haematoma and perihaematoma oedema on medical images provides quantitative outcome measures for clinical trials and may provide important markers of prognosis in people with SICH.

Methods
We take advantage of improved contrast seen on magnetic resonance (MR) images of patients with acute and early subacute SICH and introduce an automated algorithm for haematoma and oedema segmentation from these images. To our knowledge, there is no previously proposed segmentation technique for SICH that utilises MR images directly. The method is based on shape and intensity analysis for haematoma segmentation and voxel-wise dynamic thresholding of hyper-intensities for oedema segmentation.

Results
Using Dice scores to measure segmentation overlaps between labellings yielded by the proposed algorithm and five different expert raters on 18 patients, we observe that our technique achieves overlap scores that are very similar to those obtained by pairwise expert rater comparison. A further comparison between the proposed method and a state-of-the-art Deep Learning segmentation on a separate set of 32 manually annotated subjects confirms the proposed method can achieve comparable results with very mild computational burden and in a completely training-free and unsupervised way.

Conclusion
Our technique can be a computationally light and effective way to automatically delineate haematoma and oedema extent directly from MR images. Thus, with increasing use of MR images clinically after intracerebral haemorrhage this technique has the potential to inform clinical practice in the future.

Journal Article Type Article
Acceptance Date Jan 24, 2019
Online Publication Date Jan 29, 2019
Publication Date Mar 1, 2019
Deposit Date Feb 6, 2019
Publicly Available Date Feb 6, 2019
Journal Computers in Biology and Medicine
Print ISSN 0010-4825
Electronic ISSN 1879-0534
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 106
Pages 126-139
DOI https://doi.org/10.1016/j.compbiomed.2019.01.022
Keywords Brain MRI; Image segmentation; Spontaneous intracerebral haemorrhage; Stroke
Public URL https://nottingham-repository.worktribe.com/output/1522939
Publisher URL https://www.sciencedirect.com/science/article/pii/S0010482519300289#ack0010
Additional Information This article is maintained by: Elsevier; Article Title: Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage; Journal Title: Computers in Biology and Medicine; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.compbiomed.2019.01.022; Content Type: article; Copyright: © 2019 The Authors. Published by Elsevier Ltd.

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