NeuroData Image Registration

Reggie is NeuroData's open-source Python package for performing automated nonlinear image registration using LDDMM.

Reggie stands out for its ability to predict and correct for artifacts and image nonuniformity, perform registrations across image modalities, and ease of use.

  1. D. Tward, X. Li, B. Huo, B. Lee, P. Mitra, and M. Miller. 3D Mapping of Serial Histology Sections with Anamolies Using a Novel Robust Deformable Registration Algorithm. Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy, 2019.
  2. A. Branch, D. Tward, J. T. Vogelstein, Z. Wu, and M. Gallagher. An optimized protocol for iDISCO+ rat brain clearing, imaging, and analysis. bioRxiv, 2019.
  3. D. J. Tward, T. Brown, Y. Kageyama, J. Patel, Z. Hou, S. Mori, M. Albert, J. Troncoso, and M. Miller. Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer's disease. bioRxiv, 2019.
  4. D. Tward and M. Miller. EM-LDDMM for 3D to 2D registration. bioRxiv, 2019.
  5. K. S. Kutten, N. Charon, M. I. Miller, J. T. Ratnanather, K. Deisseroth, L. Ye, and J. T. Vogelstein. A Diffeomorphic Approach to Multimodal Registration with Mutual Information: Applications to CLARITY Mouse Brain Images. Medical Image Computing and Computer Assisted Intervention, 2017.
  6. K. S. Kutten, J. T. Vogelstein, N. Charon, L. Ye, K. Deisseroth, and M. I. Miller. Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM. Society of Photo-Optical Instrumentation Engineers Europe, 2016.