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iBEAT V2.0: Infant Brain Extraction and Analysis Toolbox

A new version of iBEAT (Infant Brain Extraction and Analysis Toolbox) is now available online, which is developed with latest advanced techniques (including deep learning) at the University of North Carolina at Chapel Hill. iBEAT V2.0 Cloud can handle pediatric brain images from multiple sites with various scanners and protocols. Users can process any brain structural images from birth through adolescence, including images during the first postnatal year, which typically exhibit low tissue contrast and dynamic appearance and size changes, by simply uploading images (T1w images, or T2w images, or both) into iBEAT V2.0 Cloud. All uploaded data will be securely managed in the iBEAT V2.0 web server and will not be distributed to public. 

Update: We have successfully processed 53,337 subjects.

News: To better serve the community, we are releasing the beta version of iBEAT V2.0 Docker: here. (If you have applied but did not receive any reply, please kindly remind us.)

News: Our iBEAT has contributed to 50+ journal publicationsincluding Neuron, Brain, Nature Methods, Nature Communications, PNAS, Neuroimage, Cell Reports, and IEEE TMI.

Current functionality includes:

  1. Inhomogeneity correction
  2. Skull stripping
  3. Tissue segmentation/hippocampal subfield segmentation
  4. Left/Right hemisphere separation
  5. Topology correction
  6. Cortical surface reconstruction
  7. Cortical surface measurement
  8. Cortical surface parcellation
We will further include cerebellum segmentation/parcellation, and whole brain parcellation.


About iBEAT V2.0

iBEAT V2.0 is a toolbox for processing pediatric brain MR images, using multimodality (including T1w and T2w) or single-modality. The software is developed by the Developing Brain Computing Lab, and the Brain Research through Analysis and Informatics of Neuroimaging (BRAIN) Lab in the University of North Carolina at Chapel Hill. iBEAT was first developed in 2012 (led by Dr. Dinggang Shen), now re-developed with more advanced techniques (led by Dr. Li Wang and Dr. Gang Li). 


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

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