Automatic segmentation and quantitative analysis of brain CT volume in 2-year-olds using deep learning model
This study highlights the effectiveness of deep learning for automatic brain segmentation in pediatric CT imaging, providing a reliable reference for normative brain volumes in 2-year-old children. The findings may serve as a benchmark for clinical assessment and research, complementing existing MRI-based reference data and addressing the need for accessible, population-based standards in pediatric neuroimaging.
Authors
- Feng Zhou (ORCID: https://orcid.org/0000-0002-5513-743X)
- Fengjun Xi
- Liyun Tu (ORCID: https://orcid.org/0000-0002-3389-400X)
- Jun Ma (ORCID: https://orcid.org/0000-0002-9739-0855)
- Yun Peng (ORCID: https://orcid.org/0000-0002-0872-4025)
- Yan-Jie Zhou (ORCID: https://orcid.org/0000-0001-7191-4449)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Capital Medical University (CN)
- Beijing Tian Tan Hospital (CN)
- Beijing Children’s Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-04-24
- DOI
- https://doi.org/10.3389/fneur.2025.1573060
- Citations
- 1
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.43