Mapping fetal brain development based on automated segmentation and 4D brain atlasing
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Abstract
Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23 and 36 weeks of gestation. The proposed pipeline enabled the fully automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for the quantitative characterization of fetal brain development, especially in the Chinese population.
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The lateral PFC is mostly involved in language and executive processing, while the orbitofrontal and medial PFC contribute to cognitive functioning and emotional control.
“The lPFC is mostly involved in language and executive processing, while the oFC and mPFC are known to contribute to cognitive functioning and emotional control [1–4].”
From Development of prefrontal cortex (2022) · cites this paper as [4]
The Discussion (page unavailable) suggests that changes in the superior and frontal gyri may relate to higher-order functions, but it does not assign language, executive processing, cognition, or emotional control to the specified prefrontal regions. Searches for those functions and for lateral, orbitofrontal, and medial prefrontal cortex found no passage addressing the claim. (LLM, conf=0.74)
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- Authors
- Li, H., Yan, G., Luo, W., Liu, T., Wang, Y., Liu, R., Zheng, W., Zhang, Y., Li, K., Zhao, L., Limperopoulos, C., Zou, Y., & Wu, D.
- Affiliations
- Zhejiang University (CN)
- Women's Hospital, School of Medicine, Zhejiang University (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Children's National (US)
- Corresponding author
- Wu, Dan
- ORCID
- Venue
- Brain Structure and Function
- Published
- 2021-05-29
- Volume
- 226
- Issue
- 6
- Pages
- 1961-1972
- ISSN
- 1863-2653 (ISSN-L)
- 1863-2661
- OpenAlex
- W3164909817
- Cited by
- 41
- References
- 64
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 9 · 2023: 11 · 2022: 7 · 2021: 3 · 2020: 1
- FWCI
- 2.69
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Brain Structure and Function (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and Technology of the People’s Republic of China (2018YFE0114600)
- National Natural Science Foundation of China (61801424, 81971606, 61801421, and 81971605)
- Leading Innovation and Entrepreneurship Team of Zhejiang Province (202006140)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- fetal brain development, fetal brain MRI, automated brain segmentation, super-resolution reconstruction, volumetric reconstruction, U-Net, iterative registration, gestational age, Chinese population
- MeSH terms
- Brain, Female, Fetus, Humans, Magnetic Resonance Imaging, Pregnancy, Fetal Development, Neuroimaging
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