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iBEAT: Infant Brain Extraction and Analysis Toolbox

The infant brain undergoes a dynamic expansion as the baby was born. The magnetic resonance (MR) imaging enables us to harmlessly track and monitor this both neurological and anatomical important brain development trajectory in-vivo. However, due to the undergoing myelination procedure and fast brain expansion speed, the quantitative analysis of the structural infant brain MR images meets several notorious challenges, including poor tissue contrast and dynamic brain appearance changing. To meet these challenges, the infant brain structural image processing pipeline is developed, which is dedicated to facilitating the infant structural brain MR image (mainly the T1-weighted and / or the T2-weighted image) processing.

Current functions include:

  1. T1/T2-weighted images alignment
  2. Skull stripping
  3. Inhomogeneity correction
  4. Tissue segmentation [1]
  5. Topology Correction [2]
  6. Surface Reconstruction [3]
  7. Surface Measurement
  8. Surface Parcellation
We will further include cerebellum segmentation/parcellation, and hippocampal subfield segmentation.

[1]. Wang et al., “Volume-based Analysis of 6-month-old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis”, MICCAI 2018, 1072:411-419, 2018.  [PDF]
[2]. Sun et al., "Topological Correction of Infant White Matter Surfaces Using Anatomically Constrained Convolutional Neural Network", NeuroImage, 2019.
[3]. Li et al., "Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces", NeuroImage, 90: 266-279, 2014.

About iBEAT

iBEAT is a toolbox for processing infant brain MR images, using multimodality (including T1w and T2w) or single-modality. Main functions of the software (step by step) include image preprocessing, brain extraction, tissue segmentation and brain labeling. The software is developed by the IDEA group at the University of North Carolina at Chapel Hill. iBEAT was first developed in 2012, now re-developed with more advanced techniques.


Dr. Li Wang: li_wang@med.unc.edu
Dr. Gang Li: gang_li@med.unc.edu
130 Mason Farm Road
Chapel Hill, NC 27599
United States

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