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CloudReg: automatic terabyte-scale cross-modal brain volume registration

Nature Methods volume 18, pages 845–846 (2021)Cite this article

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To the Editor — Imaging methods such as magnetic resonance imaging (MRI), micro-computed tomography (microCT) and light-sheet microscopy (LSM) of cleared tissue samples can generate intact anatomic and molecular whole-brain data. However, each modality produces unique artifacts based on the physical principles of the technique, including intensity inhomogeneity due to magnetic field bias in MRI or microscope optics in LSM and beam hardening in microCT1,2,3. These artifacts and the size of the datasets generated pose a substantial challenge in data handling, cross-modal image registration, and analysis. Visualization and anatomically relevant analysis of high-resolution, multi-field-of-view (mFOV) datasets require preprocessing to remove artifacts, stitching into a complete volume, and registration to a reference atlas3,4.Each step presents specific challenges. First, stitching acquired fields of view (FOVs) into a complete volume is computation and time intensive. Second, preprocessing requires correcting artifacts unique to each modality and sample. Third, registration methods are often intramodal, have manual components, or are limited by artifacts introduced by specimen preparation and imaging5,6. Finally, visualization of these terabyte-scale datasets on a local machine can be computationally intensive7.

To address these challenges, we present CloudReg, an automatic, cross-modal, cloud-based pipeline that performs local and global intensity correction, stitching, nonlinear image registration and interactive online visualization through Neuroglancer (https://github.com/google/neuroglancer)4,8,9,. We combine state-of-the-art, open-source tools with algorithms for distributed local intensity correction and cross-modal registration developed in this work. We applied CloudReg to various datasets acquired with in vivo human brain MRI, ex vivo macaque brain MRI, ex vivo in situ mouse brain microCT, and cleared mouse and rat brains imaged with LSM.

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Fig. 1: CloudReg.

Data availability

The datasets in this study are available from the corresponding author on reasonable request.

Code availability

The CloudReg pipeline is open-source and available under an Apache 2.0 license at https://github.com/neurodata/CloudReg and at https://doi.org/10.5281/zenodo.4949737 (ref. 11).

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Acknowledgements

This work was supported by R01 AG066184/AG/NIA NIH HHS/United States and by the National Science Foundation (NSF) under NSF Award Number EEC-1707298. The authors would also like to thank Microsoft Research for supporting this work. V.C. was supported by UPENN/NIH grant 133284. D.J.T. was supported by the Kavli Neuroscience Discovery Institute, the Karen Toffler Charitable Trust through the Toffler Scholar Program, and the NIH (U19MH114821). A.K.C. was supported by DARPA grant W911NF-14-2-0013 and NIMH TR01 R01 MH099647. M.A.W. was supported by NIDDK grant K08MH113039, DARPA grant W911NF-14-2-0013 and NIMH TR01 R01 MH099647. B.Y.H. was supported by DARPA grant W911NF-14-2-0013 and NIMH TR01 R01 MH099647. F.G. was supported by NARSAD Young Investigator Award from BBRF, K99 from NIDA (1K99DA050662-01), DARPA grant W911NF-14-2-0013 and NIMH TR01 R01 MH099647. T.A.M. was supported by the AP Giannini Foundation, a Stanford Dean’s Fellowship, NIH/NINDS (K99-NS116122), DARPA grant W911NF-14-2-0013 and NIMH TR01 R01 MH099647. A.B. was supported by National Institute of Health (NIH) (grant P01AG009973 and the Johns Hopkins University Kavli Neuroscience Discovery Institute Postdoctoral Fellowship. J.S.R. was supported in part by the intramural program of the NCI at the NIH. K.D. was supported by DARPA grant W911NF-14-2-0013 and NIMH TR01 R01 MH099647. J.T.V. was supported by R01 AG066184/AG/NIA NIH HHS/United States, EEC-1707298, UPENN/NIH grant 133284 and Microsoft Research.

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Authors and Affiliations

  1. Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA

    Vikram Chandrashekhar, Devin Crowley & Joshua T. Vogelstein

  2. Department of Computational Medicine and Neurology, University of California Los Angeles, Los Angeles, CA, USA

    Daniel J. Tward

  3. CNC Program, Stanford University, Stanford, CA, USA

    Ailey K. Crow, Brian Y. Hsueh, Felicity Gore, Timothy A. Machado & Karl Deisseroth

  4. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Matthew A. Wright, Brian Y. Hsueh, Felicity Gore, Timothy A. Machado & Karl Deisseroth

  5. Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA

    Audrey Branch

  6. Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Jared S. Rosenblum

Authors
  1. Vikram Chandrashekhar
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  12. Joshua T. Vogelstein
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Contributions

V.C. conceived the study, drafted the original manuscript, performed data analysis, built a reproducible pipeline and revised the manuscript. D.J.T. conceived the study, revised the manuscript, performed data analysis and supervised data analysis. D.C. ported registration algorithm to Python, performed data analysis and revised the manuscript. A.K.C., M.A.W., B.Y.H., F.G., T.A.M. and A.B. provided CLARITY/iDISCO/SHIELD LSM data and revised the manuscript. J.S.R. helped draft the original manuscript, revised the manuscript, provided data and placed landmarks for determining registration accuracy. K.D. and J.T.V. conceived the study, supervised data analysis and revised the manuscript.

Corresponding author

Correspondence to Joshua T. Vogelstein.

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Competing interests

The authors declare no competing interests.

Supplementary information

Supplementary Information

Supplementary Note, Figures 1–5 and References

Video 1

The progression of our registration over 5,000 iterations is shown. Our input LSM-imaged CLARITY mouse brain is shown in magenta and the ARA is shown in green. The overlap between the input data and the atlas is shown in grayscale. LSM, light-sheet microscopy; CLARITY, clear lipid-exchanged anatomically rigid imaging/immunostaining-compatible tissue hydrogel; ARA, Allen Reference Atlas.

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Chandrashekhar, V., Tward, D.J., Crowley, D. et al. CloudReg: automatic terabyte-scale cross-modal brain volume registration. Nat Methods 18, 845–846 (2021). https://doi.org/10.1038/s41592-021-01218-z

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