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Updated: May 27, 2026

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Published on: May 19, 2023

Longitudinally guided level sets for consistent tissue segmentation of neonates.

Li Wang¹, Feng Shi, Pew-Thian Yap

¹IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA.

Human Brain Mapping

|December 6, 2011

Summary

This study introduces a new method for segmenting neonatal brain images, improving accuracy by using later scans to guide earlier ones. This technique enhances the analysis of brain development and diseases in infants.

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Area of Science:

Medical imaging
Neuroscience
Biomedical engineering

Background:

Accurate segmentation of white matter, grey matter, and cerebrospinal fluid in neonatal brains is crucial for assessing development and disease.
Neonatal brain imaging presents challenges due to low tissue contrast, making segmentation difficult with traditional methods.
Existing methods often rely on single time-point data, neglecting valuable longitudinal scan information.

Purpose of the Study:

To develop a novel, consistent neonatal brain image segmentation method.
To leverage longitudinal data and anatomical patterns for improved segmentation accuracy.
To address the limitations of current single-time-point voxel-based approaches.

Main Methods:

A longitudinally guided level-sets method was proposed, integrating local intensity, atlas priors, cortical thickness, and longitudinal information.

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The method utilizes a variational framework with an energy functional derived from variational principles.

Segmentation of a later time-point image guides the segmentation of the neonatal image.

Main Results:

The proposed method demonstrated promising results in segmenting neonatal brain images.
Validation was performed using both simulated and in vivo neonatal brain datasets.
The technique showed improved consistency and accuracy compared to existing approaches.

Conclusions:

The longitudinally guided level-sets method offers a robust solution for consistent neonatal brain image segmentation.
This approach effectively overcomes the challenges posed by low tissue contrast in neonatal imaging.
The method holds potential for advancing the quantitative analysis of brain development and pathologies in neonates.