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Updated: May 1, 2026

State of the Art Cranial Ultrasound Imaging in Neonates

State of the Art Cranial Ultrasound Imaging in Neonates

Published on: February 2, 2015

Neonatal brain MRI Atlas: Current and future.

Haohao Zhai¹, Haoran Gao², Xiaonan Zhuo¹

¹School of Information Science and Engineering, Shandong Normal University, Jinan, China.

Journal of Neuroscience Methods

|April 29, 2026

Summary

This summary is machine-generated.

This review details the evolution of neonatal brain atlases, from static templates to deep learning models. These advancements improve the study of early brain development and neuroimaging analysis.

Keywords:

Deep learning MRI Neonatal brain atlas Template construction

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Area of Science:

Neuroimaging
Developmental Neuroscience
Medical Image Analysis

Background:

Neonatal brain atlases are crucial for understanding early brain development.
Studying the rapidly changing neonatal brain presents unique challenges.
Existing atlas construction methods have limitations in accuracy and adaptability.

Purpose of the Study:

To provide a comprehensive review of the methodological evolution of neonatal brain template construction.
To discuss how these methods address challenges in neonatal neuroimaging.
To highlight future directions for neonatal brain atlas development.

Main Methods:

Review of five stages of neonatal brain atlas construction: static, iterative, spatiotemporal, probabilistic/multimodal, and deep learning.
Analysis of methodological shifts from linear registration to deep learning architectures.
Examination of atlas evaluation frameworks and persistent challenges.

Main Results:

Neonatal brain atlas construction has evolved significantly, mirroring advancements in neuroimaging and computational methods.
Methodological developments address physiological changes and MR acquisition challenges.
Persistent challenges include anatomical variability, limited data, and hardware constraints.

Conclusions:

The evolution of neonatal brain atlases has enhanced neuroimaging analysis for early development.
Future work should focus on personalized atlases, generative models, and unified benchmarking.
Improved atlases will support more reliable developmental assessments and clinical applications.