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The Developing Brain Computing (DBC) Lab’s research mainly focuses on developing innovative computational methods and tools for processing and analyzing medical imaging data, especially for lifespan brain MRIs.
Highlight 1: Infant Brain Extraction and Analysis Toolbox (iBEAT Cloud)
We have been focusing on infant brain processing since 2007. In 2020, a new version of iBEAT (Infant Brain Extraction and Analysis Toolbox) is now available online as iBEAT V2.0 Cloud (http://www.ibeat.cloud), which is developed with the latest advanced techniques (including deep learning). Up to date, we have successfully processed 30,000+ infant brain images and received numerous praise/positive feedback from 200+ institutions, including Boston Children’s Hospital/Harvard Medical School, Stanford University, Yale University, University of Maryland, University of California, University of Pennsylvania, Washington University in St. Louis, Tokyo Metropolitan University, Arkansas Children’s Research Institute, and Princeton University. iBEAT Cloud has directly contributed to 50+ high-impact journal publications (including Neuron, Nature Methods, Nature Communications, PNAS, Neuroimage, Cell Reports, IEEE TMI, etc.) and 20+ conferences/abstracts (including ICCV, MICCAI, ISMRM, OHBM, etc.)
Fig. 1. Illustration of the key functionality included in the iBEAT V2.0 Cloud (Li Wang, et al., Nature Protocols, vol. 18, no. 5, pp. 1488–1509, 2023)
Highlight 2: Lifespan Skull Stripping (LifespanStrip)
Limei Wang, Yue Sun, Jakob Seidlitz, Richard A. I. Bethlehem, Aaron Alexander-Bloch, Lena Dorfschmidt, Gang Li, Jed T. Elison, Weili Lin, Li Wang∗, “A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases”, in Nature Biomedical Engineering, 2025. https://doi.org/10.1038/s41551-024-01337-w [GitHub]
Highlight 3: Lifespan Brain MRI Enhancement Foundation Model (BME-X)
Yue Sun, Limei Wang, Gang Li, Weili Lin, Li Wang∗, “A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks”, in Nature Biomedical Engineering, 2024. https://doi.org/10.1038/s41551-024-01283-7 [GitHub]
Highlight 4: Cerebellum Segmentation
Yue Sun, Limei Wang, Kun Gao, Shihui Ying, Weili Lin, Kathryn L. Humphreys, Gang Li, Sijie Niu, Mingxia Liu*, Li Wang∗, “Self-Supervised Learning with Application for Infant Cerebellum Segmentation and Analysis”, in Nature Communications, vol. 14, 4717, 2023.
