Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury.
Brain Inj
; 30(12): 1458-1468, 2016.
Article
em En
| MEDLINE
| ID: mdl-27834541
ABSTRACT
BACKGROUND:
White matter hyperintensities (WMHs) are foci of abnormal signal intensity in white matter regions seen with magnetic resonance imaging (MRI). WMHs are associated with normal ageing and have shown prognostic value in neurological conditions such as traumatic brain injury (TBI). The impracticality of manually quantifying these lesions limits their clinical utility and motivates the utilization of machine learning techniques for automated segmentation workflows.METHODS:
This study develops a concatenated random forest framework with image features for segmenting WMHs in a TBI cohort. The framework is built upon the Advanced Normalization Tools (ANTs) and ANTsR toolkits. MR (3D FLAIR, T2- and T1-weighted) images from 24 service members and veterans scanned in the Chronic Effects of Neurotrauma Consortium's (CENC) observational study were acquired. Manual annotations were employed for both training and evaluation using a leave-one-out strategy. Performance measures include sensitivity, positive predictive value, [Formula see text] score and relative volume difference.RESULTS:
Final average results were sensitivity = 0.68 ± 0.38, positive predictive value = 0.51 ± 0.40, [Formula see text] = 0.52 ± 0.36, relative volume difference = 43 ± 26%. In addition, three lesion size ranges are selected to illustrate the variation in performance with lesion size.CONCLUSION:
Paired with correlative outcome data, supervised learning methods may allow for identification of imaging features predictive of diagnosis and prognosis in individual TBI patients.Palavras-chave
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento Eletrônico de Dados
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Substância Branca
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Aprendizado de Máquina Supervisionado
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Lesões Encefálicas Traumáticas
Tipo de estudo:
Diagnostic_studies
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Etiology_studies
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Guideline
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adolescent
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Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Brain Inj
Assunto da revista:
CEREBRO
Ano de publicação:
2016
Tipo de documento:
Article