A deep learning model for brain segmentation across pediatric and adult populations.
Sci Rep
; 14(1): 11735, 2024 05 22.
Article
em En
| MEDLINE
| ID: mdl-38778071
ABSTRACT
Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across various life stages. However, existing solutions are specifically designed for certain age ranges, limiting their applicability in monitoring brain development from infancy to late adulthood. This retrospective study aims to develop and validate a brain segmentation model across pediatric and adult populations. First, we trained a deep learning model to segment tissues and brain structures using T1-weighted MR images from 390 patients (age range 2-81 years) across four different datasets. Subsequently, the model was validated on a cohort of 280 patients from six distinct test datasets (age range 4-90 years). In the initial experiment, the proposed deep learning-based pipeline, icobrain-dl, demonstrated segmentation accuracy comparable to both pediatric and adult-specific models across diverse age groups. Subsequently, we evaluated intra- and inter-scanner variability in measurements of various tissues and structures in both pediatric and adult populations computed by icobrain-dl. Results demonstrated significantly higher reproducibility compared to similar brain quantification tools, including childmetrix, FastSurfer, and the medical device icobrain v5.9 (p-value< 0.01). Finally, we explored the potential clinical applications of icobrain-dl measurements in diagnosing pediatric patients with Cerebral Visual Impairment and adult patients with Alzheimer's Disease.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Encéfalo
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Imageamento por Ressonância Magnética
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Aprendizado Profundo
Limite:
Adolescent
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Adult
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Aged
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Aged80
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Child
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Child, preschool
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Bélgica