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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

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4D multi-modality tissue segmentation of serial infant images.

Li Wang¹, Feng Shi, Pew-Thian Yap

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

|October 11, 2012

Summary

This study introduces a new method for segmenting infant brain MR images, improving accuracy in early development studies. The technique enhances consistency across multiple scans, crucial for tracking brain changes over time.

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Area of Science:

Neuroimaging
Medical Image Analysis
Developmental Neuroscience

Background:

Accurate infant brain MRI segmentation is vital for understanding early brain development.
Rapid changes in myelination during infancy cause significant contrast variations, challenging segmentation.
Isointense gray and white matter around 6-8 months pose particular difficulties.

Purpose of the Study:

To develop a robust method for segmenting serial infant brain MR images from 2 weeks to 1.5 years.
To address the challenges posed by low contrast and intensity inversions in infant brain development.
To improve the accuracy and consistency of automated brain segmentation in longitudinal studies.

Main Methods:

A longitudinally guided level set method utilizing multi-modal MRI (T1, T2, diffusion-weighted) at each time point.
Incorporation of a longitudinally consistent term to ensure temporal coherence in segmentation.
Validation on 28 infant subjects with 5 longitudinal scans each.

Main Results:

The proposed method achieved significantly higher Dice Ratios compared to single-time-point and voxel-wise longitudinal methods.
Demonstrated superior performance in segmenting infant brain MR images, including those with low contrast.
Automated segmentations closely matched manual ground-truth across longitudinal data.

Conclusions:

The longitudinally guided level set method provides accurate and consistent infant brain MRI segmentation.
This approach is effective in overcoming the challenges of rapid brain maturation and myelination.
Publicly available software facilitates its application in research and clinical settings.