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.
- 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.
- 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.
- 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.
- D. Tward and M. Miller. EM-LDDMM for 3D to 2D registration. bioRxiv, 2019.
- K. S. Kutten, N. Charon, M. I. Miller, J. T. Ratnanather, J. Matelsky, A. D. Baden, K. Lillaney, K. Deisseroth, L. Ye, and J. T. Vogelstein. A large deformation diffeomorphic approach to registration of CLARITY images via mutual information. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017.
- 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. Optics, Photonics and Digital Technologies for Imaging Applications IV, 2016.