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dc.contributor.authorGallagher, Rebecca G
dc.contributor.authorSwienton, David J
dc.contributor.authorMorgan, Paul S
dc.contributor.authorDineen, Robert A
dc.date.accessioned2022-10-25T15:46:16Z
dc.date.available2022-10-25T15:46:16Z
dc.date.issued2019
dc.identifier.citationPszczolkowski, S., Law, Z.K., Gallagher, R.G., Meng, D., Swienton, D.J., Morgan, P.S., Bath, P.M., Sprigg, N. and Dineen, R.A. (2019) 'Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage', Computers in biology and medicine, 106, pp. 126-139. doi: https://dx.doi.org/10.1016/j.compbiomed.2019.01.022.en_US
dc.identifier.issn1879-0534
dc.identifier.urihttp://hdl.handle.net/20.500.12904/15884
dc.description.abstractBACKGROUND: 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. Copyright © 2019. Published by Elsevier Ltd.
dc.description.urihttps://dx.doi.org/10.1016/j.compbiomed.2019.01.022en_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.subjectHaematomaen_US
dc.subjectMagnetic resonance imagingen_US
dc.subjectStrokeen_US
dc.titleAutomated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhageen_US
dc.typeArticleen_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.versionVoRen_US
rioxxterms.versionofrecord10.1016/j.compbiomed.2019.01.022en_US
rioxxterms.typeJournal Article/Reviewen_US
refterms.dateFCD2022-10-25T15:46:17Z
refterms.versionFCDVoR
refterms.dateFOA2022-10-25T15:46:17Z
refterms.panelUnspecifieden_US
refterms.dateFirstOnline2019
html.description.abstractBACKGROUND: 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. Copyright © 2019. Published by Elsevier Ltd.en_US
rioxxterms.funder.project94a427429a5bcfef7dd04c33360d80cden_US


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