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 31,454 infant brain 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 publications, including 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.