White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds.
Neuroimage
; 237: 118140, 2021 08 15.
Article
em En
| MEDLINE
| ID: mdl-33957235
ABSTRACT
White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging processes, cognitive decline, and dementia. In the current study, we proposed a U-Net with multi-scale highlighting foregrounds (HF) for WMHs segmentation. Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects. We evaluated the segmentation performance of the proposed approach using the Challenge training dataset. Then we assessed the clinical utility of the WMH volumes that were automatically computed using our method and the Alzheimer's Disease Neuroimaging Initiative database. We demonstrated that the U-Net with HF significantly improved the detection of the WMH voxels at the boundary of the WMHs or in small WMH clusters quantitatively and qualitatively. Up to date, the proposed method has achieved the best overall evaluation scores, the highest dice similarity index, and the best F1-score among 39 methods submitted on the WMH Segmentation Challenge that was initially hosted by MICCAI 2017 and is continuously accepting new challengers. The evaluation of the clinical utility showed that the WMH volume that was automatically computed using U-Net with HF was significantly associated with cognitive performance and improves the classification between cognitive normal and Alzheimer's disease subjects and between patients with mild cognitive impairment and those with Alzheimer's disease. The implementation of our proposed method is publicly available using Dockerhub (https//hub.docker.com/r/wmhchallenge/pgs).
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Envelhecimento
/
Imageamento por Ressonância Magnética
/
Leucoaraiose
/
Doença de Alzheimer
/
Neuroimagem
/
Disfunção Cognitiva
/
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
Limite:
Aged
/
Aged80
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Female
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Humans
/
Male
Idioma:
En
Revista:
Neuroimage
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Coréia do Sul