Automatic veins analysis of susceptibility weighted image in hypoxic-ischaemic encephalopathy
Automatic veins analysis of susceptibility weighted image in hypoxic-ischaemic encephalopathy
Background and objective: the purpose of this study is to evaluate venous vascular structure and distribution as prognostic indicators of developmental outcomes for infants with neonatal hypoxic-ischaemic encephalopathy (HIE) by detecting and analysing ridges representing vessels on susceptibility-weighted magnetic resonance images (SWIs).
Methods: forty-two infants with neonatal HIE underwent SWI in the neonatal period and neurodevelopmental assessment at age 2 years. Normalised histograms of the width, intensity, length and Hessian eigenvalues extracted from the ridge analysis of each patient's SWI are applied as feature vectors to feed into supervised classifiers such as the kNN and random forest (RF) classifiers to predict their neurodevelopmental outcomes. Here we also propose a supervised classifier for automatic prognosis of automated detection of SWI signs of HIE. Our classifier proposed in this paper demonstrates a superior performance in HIE prognosis for the datasets associated with cognitive and motor outcomes and it also enables to determination of brain regions which have been affected by hypoxia-ischaemia by extracting appropriate features from SWI images.
Results: the feature vectors containing width, intensity, length, and eigenvalue show a promising classification accuracy of 78.67% ± 2.58Linear regression, polynomial regression, and support vector regression (SVR) models predicted outcomes and the lower mean relative errors (MRE) for motor and cognitive outcomes are 0.088 ± 0.073 and 0.101 ± 0.11 respectively.
Conclusion: the features derived from the vascular ridges improve the prognostic value of SWI in HIE. Our findings suggest that it is possible to predict neurological, motor, and cognitive outcomes by numerical analysis of neonatal SW images and to identify brain regions on SWI affected by hypoxia-ischaemia.
Cognitive outcomes, Hypoxic-ischaemic encephalopathy, Motor, Neurological, Ridge detection, Susceptibility-weighted imaging, sasan, mahmoodi
83-96
Tang, Zhen
a8a50743-212f-4fb2-84f1-805f0758a83e
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Darekar, Angela
327a5432-d7d2-4ce6-ab2b-0d5db86298c3
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba
21 January 2023
Tang, Zhen
a8a50743-212f-4fb2-84f1-805f0758a83e
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Darekar, Angela
327a5432-d7d2-4ce6-ab2b-0d5db86298c3
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba
Tang, Zhen, Mahmoodi, Sasan, Darekar, Angela and Vollmer, Brigitte
(2023)
Automatic veins analysis of susceptibility weighted image in hypoxic-ischaemic encephalopathy.
Magnetic Resonance Imaging, 98, .
(doi:10.1016/j.mri.2023.01.014).
Abstract
Background and objective: the purpose of this study is to evaluate venous vascular structure and distribution as prognostic indicators of developmental outcomes for infants with neonatal hypoxic-ischaemic encephalopathy (HIE) by detecting and analysing ridges representing vessels on susceptibility-weighted magnetic resonance images (SWIs).
Methods: forty-two infants with neonatal HIE underwent SWI in the neonatal period and neurodevelopmental assessment at age 2 years. Normalised histograms of the width, intensity, length and Hessian eigenvalues extracted from the ridge analysis of each patient's SWI are applied as feature vectors to feed into supervised classifiers such as the kNN and random forest (RF) classifiers to predict their neurodevelopmental outcomes. Here we also propose a supervised classifier for automatic prognosis of automated detection of SWI signs of HIE. Our classifier proposed in this paper demonstrates a superior performance in HIE prognosis for the datasets associated with cognitive and motor outcomes and it also enables to determination of brain regions which have been affected by hypoxia-ischaemia by extracting appropriate features from SWI images.
Results: the feature vectors containing width, intensity, length, and eigenvalue show a promising classification accuracy of 78.67% ± 2.58Linear regression, polynomial regression, and support vector regression (SVR) models predicted outcomes and the lower mean relative errors (MRE) for motor and cognitive outcomes are 0.088 ± 0.073 and 0.101 ± 0.11 respectively.
Conclusion: the features derived from the vascular ridges improve the prognostic value of SWI in HIE. Our findings suggest that it is possible to predict neurological, motor, and cognitive outcomes by numerical analysis of neonatal SW images and to identify brain regions on SWI affected by hypoxia-ischaemia.
Text
Automatic veins analysis of susceptibility weighted image in hypoxic ischaemic encephalopathy
- Accepted Manuscript
More information
Accepted/In Press date: 14 January 2023
e-pub ahead of print date: 18 January 2023
Published date: 21 January 2023
Keywords:
Cognitive outcomes, Hypoxic-ischaemic encephalopathy, Motor, Neurological, Ridge detection, Susceptibility-weighted imaging, sasan, mahmoodi
Identifiers
Local EPrints ID: 475909
URI: http://eprints.soton.ac.uk/id/eprint/475909
ISSN: 0730-725X
PURE UUID: 8ff94bbf-c19d-43d7-bfa7-c8720e29b72b
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Date deposited: 30 Mar 2023 16:39
Last modified: 17 Mar 2024 07:39
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Contributors
Author:
Zhen Tang
Author:
Sasan Mahmoodi
Author:
Angela Darekar
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