Log in

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. 

News: We have successfully processed 7644 infant brain subjects.

News: To better serve the community, we are actively packing the pipeline as a Docker version and will release it once our pipeline paper is accepted.

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.


  

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 Baby Brain Mapping Lab in the University of North Carolina at Chapel Hill. iBEAT was first developed in 2012, now re-developed with more advanced techniques. 

Contacts

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


Copyright © UNC                      Terms of Use

Powered by Wild Apricot Membership Software