Published September 22, 2022 | Version v1.0.0
Software Open

BIBSNet

Creators

  • 1. Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN
  • 2. Washington University, St. Louis, MO
  • 3. Department of Psychology, Stanford University, Stanford, CA
  • 4. University of Minnesota, Minneapolis, MN

Description

Quickly and accurately segments an optimally-aligned T1 and T2 pair with a deep neural network trained via nnU-Net and SynthSeg with a large 0 to 8 month old infant MRI brain dataset.

This application utilizes the following software: nvidia/pytorch:21.11-py3, nnU-Net, SynthSeg, SynthStrip

Notes

FUNDING SOURCES 1U01DA055371 Wilson (PI) 09/01/21 – 08/31/26 17/24 The Healthy Brain and Child Development National Consortium U24 DA055330 Smyser (PI) 09/30/21 – 06/30/26 Healthy brain and child development national consortium data coordinating center Sub 1016803_ UMINN/U01DA041148 Fair & Nagel (PI) 04/15/20 – 03/31/27 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT OHSU UG3 OD023349 Fair (Co-PI) 09/21/16–08/31/23 Pre- and Postnatal Exposure Periods for Child Health: Common Risks and Shared Mechanisms 1R37MH125829-01 Fair, Satterwaithe (PI) 09/01/21 – 03/31/26 Precision mapping of individualized executive networks in youth 2R01MH096773-08A1 Fair & Dosenbach (PI) 06/05/20– 03/31/25 Identification of outcome-based sub-populations using deep phenotyping and precision functional mapping across ADHD and ASD

Files

DCAN-Labs/BIBSnet-v1.0.0.zip

Files (8.5 kB)

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Additional details

Related works

Is supplement to
https://github.com/DCAN-Labs/BIBSnet/tree/v1.0.0 (URL)

References

  • Billot, Benjamin, Douglas N. Greve, Oula Puonti, Axel Thielscher, Koen Van Leemput, Bruce Fischl, Adrian V. Dalca, and Juan Eugenio Iglesias. 2021. "SynthSeg: Domain Randomisation for Segmentation of Brain Scans of Any Contrast and Resolution." arXiv [eess.IV]. arXiv. http://arxiv.org/abs/2107.09559.
  • Howell, Brittany R., Martin A. Styner, Wei Gao, Pew-Thian Yap, Li Wang, Kristine Baluyot, Essa Yacoub, et al. 2019. "The UNC/UMN Baby Connectome Project (BCP): An Overview of the Study Design and Protocol Development." NeuroImage 185 (January): 891–905.
  • Isensee, Fabian, Paul F. Jaeger, Simon A. A. Kohl, Jens Petersen, and Klaus H. Maier-Hein. 2021. "nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation." Nature Methods 18 (2): 203–11.
  • Paszke, Adam, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. "Automatic Differentiation in PyTorch." https://openreview.net/pdf?id=BJJsrmfCZ.