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1. iBEAT V2.0 Cloud

A new version of iBEAT (Infant Brain Extraction and Analysis Toolbox) is now available online as iBEAT V2.0 Cloud (, which is developed by Dr. Gang Li’s and Dr. Li Wang’s teams with cutting-edge techniques (including deep learning). iBEAT V2.0 Cloud can handle pediatric brain images from different sites with various scanners and protocols. Users can process 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. All uploaded data will be securely managed in the iBEAT V2.0 web server and will not be distributed to public. The current functionality of iBEAT V2.0 Cloud includes: preprocessing, inhomogeneity correction, skull stripping, tissue segmentation, left/right hemisphere separation, topology correction, cortical surface reconstruction, cortical surface measurement, and cortical surface parcellation. iBEAT V2.0 has successfully processed 12,000+ infant brain images from 90+ institutes and consistently achieves superior results. (link)

2. UNC 4D Infant Cortical Surface Atlas

UNC 4D longitudinal infant surface atlases of cortical structures from neonates to 6 years of age contain 11 time points, including 1 month, 3 months, 6 months, 9 months, 12 months, 18 months, 24 months, 36 months, 48 months, 60 months and 72 months, thus densely covering and well characterizing the critical stages of the dynamic early brain development. (4000+ downloads on NITRC)

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications. Gang Li, Li Wang, Feng Shi, John H. Gilmore, Weili Lin, Dinggang Shen. Medical image analysis, vol. 25 (1), pp. 22-36, 2015.

3. UNC Neonate Cortical Surface Atlas

The UNC spatiotemporal neonatal cortical surface atlas focuses on newborn babies. Due to their rapid development, the atlas is built at each week, including surface atlases at 39, 40, 41, 42, 43, and 44 post-menstrual weeks. (download)

Construction of spatiotemporal neonatal cortical surface atlas using a large-scale dataset. Zhengwang Wu, Gang Li, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen. IEEE International Symposium on Biomedical Imaging (ISBI), Washington, DC, USA, April 4-7, 2018.

4. Spherical U-Net Package

Python-based spherical cortical surface processing tools, including spherical resampling, interpolation, parcellation, registration, atlas construction, etc. It provides fast and accurate cortical surface-based data analysis using deep learning techniques. (download)

Spherical U-Net on Cortical Surfaces: Methods and Applications. Fenqiang Zhao, Shunren Xia, Zhengwang Wu, Dingna Duan, Li Wang, Weili Lin, John Gilmore, Dinggang Shen, Gang Li. Information Processing in Medical Imaging (IPMI), 2019.

Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction. Fenqiang Zhao, Zhengwang Wu, Li Wang, Weili Lin, John H. Gilmore, Shunren Xia, Dinggang Shen, Gang Li. IEEE Transactions on Medical Imaging (TMI), 2021.