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Rapid image deconvolution and multiview fusion for optical microscopy

Nature Biotechnology volume 38, pages 1337–1346 (2020)Cite this article

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Abstract

The contrast and resolution of images obtained with optical microscopes can be improved by deconvolution and computational fusion of multiple views of the same sample, but these methods are computationally expensive for large datasets. Here we describe theoretical and practical advances in algorithm and software design that result in image processing times that are tenfold to several thousand fold faster than with previous methods. First, we show that an ‘unmatched back projector’ accelerates deconvolution relative to the classic Richardson–Lucy algorithm by at least tenfold. Second, three-dimensional image-based registration with a graphics processing unit enhances processing speed 10- to 100-fold over CPU processing. Third, deep learning can provide further acceleration, particularly for deconvolution with spatially varying point spread functions. We illustrate our methods from the subcellular to millimeter spatial scale on diverse samples, including single cells, embryos and cleared tissue. Finally, we show performance enhancement on recently developed microscopes that have improved spatial resolution, including dual-view cleared-tissue light-sheet microscopes and reflective lattice light-sheet microscopes.

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Fig. 1: An unmatched back projector reduces the number of iterations required for Richardson–Lucy deconvolution.
Fig. 2: Improvements in deconvolution and registration accelerate the processing of multiview light-sheet datasets.
Fig. 3: Imaging millimeter-scale cleared-tissue volumes with isotropic micrometer-scale spatial resolution.
Fig. 4: Deep learning massively accelerates deconvolution with a spatially varying PSF.

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Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

The code used in this study is available as Supplementary Software. A code description and several test datasets are also included. Users can also download the code and updates from GitHub at https://github.com/eguomin/regDeconProject; https://github.com/eguomin/diSPIMFusion; https://github.com/eguomin/microImageLib.

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Acknowledgements

We thank O. Schwartz and the Biological Imaging Section (RTB/NIAID/NIH) for supplying the confocal microscope platform and providing technical assistance with experiments, W.S. Young (NIMH) for providing early access to the V1b mouse line, J. Daniels (ASI) for advice on integrating the multi-immersion objectives into our cleared-tissue diSPIM, M. Anthony (ASI) for providing CAD drawings of our diSPIM assembly, J. Shaw (Bitplane) for help with Imaris and the neurite tracing plugin, A. Lauziere for his feedback and discussion on the neural network portion of the work, R. Christensen for testing aspects of the registration and deep learning pipelines, E. Ardiel for helping us to acquire the embryonic GCaMP3 muscle data with reflective diSPIM, N. Stuurman for advice in developing ImageJ-compatible software, R. Heintzmann for his critical evaluation of our methods and suggestions on improving the clarity of our manuscript, and H. Eden and G. Patterson for valuable feedback on the manuscript. This research was supported by the intramural research programs of the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Allergy and Infectious Diseases, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Mental Health and the National Cancer Institute within the National Institutes of Health, the National Natural Science Foundation of China (61525106, 61427807, U1809204) and the National Key Technology Research and Development Program of China (2017YFE0104000, 2016YFC1300302). V.P. and A.B. acknowledge support by the National Center for Advancing Translational Sciences of the National Institutes of Health through grant number UL1-TR000430, NSF award 1528911 (to V.P.) and NSF Graduate Research Fellowship Program grant number DGE-1144082 (to A.B.). A.U. acknowledges support from NSF awards PHY-1607645 and PHY-1806903. P.L.R. acknowledges support from NIH R01EB107293. H.S., D.C.-R. and P.L.R. acknowledge the Whitman and Fellows program at MBL for providing funding and space for discussions valuable to this work. D.C.-R., R.I., A.S., W.A.M. and Z.B. were supported by NIH grant number R24-OD016474, L.H.D. was supported by a Diversity Supplement to R24-OD016474 and M.W.M. was supported by F32-NS098616. Z.B. additionally acknowledges support via NIH grant number R01-GM097576 and the MSK Cancer Center Support/Core Grant (P30-CA008748). A.S. is additionally supported by grant 2019-198110 (5022) from the Chan Zuckerberg Initiative and the Silicon Valley Community Foundation. J.C.W. also acknowledges support from the Chan Zuckerberg Initiative.

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Author notes

  1. These authors contributed equally: Min Guo, Yue Li.

Authors and Affiliations

  1. Laboratory of High-Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA

    Min Guo, Yijun Su, Ivan Rey-Suarez, Yicong Wu & Hari Shroff

  2. State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China

    Yue Li & Huafeng Liu

  3. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    Talley Lambert & Jennifer C. Waters

  4. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Talley Lambert

  5. Section on Neural Developmental Dynamics, Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA

    Damian Dalle Nogare & Ajay B. Chitnis

  6. Departments of Neuroscience and Cell Biology, Yale University School of Medicine, New Haven, CT, USA

    Mark W. Moyle, Leighton H. Duncan, Richard Ikegami & Daniel Colón-Ramos

  7. Developmental Biology Program, Sloan Kettering Institute, New York, NY, USA

    Anthony Santella & Zhirong Bao

  8. Biophysics Program, University of Maryland, College Park, MD, USA

    Ivan Rey-Suarez & Arpita Upadhyaya

  9. Women’s Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    Daniel Green & Christina M. Annunziata

  10. Committee on Development, Regeneration and Stem Cell Biology, University of Chicago, Chicago, IL, USA

    Anastasia Beiriger & Victoria Prince

  11. Advanced Imaging and Microscopy Resource, National Institutes of Health, Bethesda, MD, USA

    Jiji Chen, Harshad Vishwasrao & Hari Shroff

  12. Biological Imaging Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Sundar Ganesan

  13. Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA

    Victoria Prince

  14. Laboratory of Muscle Stem Cells and Gene Regulation, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Bethesda, MD, USA

    Markus Hafner

  15. Department of Genetics and Genome Sciences and Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT, USA

    William A. Mohler

  16. Department of Physics, University of Maryland, College Park, MD, USA

    Arpita Upadhyaya

  17. Institute for Physical Science and Technology, University of Maryland, College Park, MD, USA

    Arpita Upadhyaya

  18. Section on Fundamental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

    Ted B. Usdin

  19. Marine Biological Laboratory Fellows Program, Woods Hole, MA, USA

    Daniel Colón-Ramos, Patrick La Riviere & Hari Shroff

  20. Instituto de Neurobiología, Recinto de Ciencias Médicas, Universidad de Puerto Rico, San Juan, Puerto Rico

    Daniel Colón-Ramos

  21. Department of Radiology, University of Chicago, Chicago, IL, USA

    Patrick La Riviere

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  1. Min Guo
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Contributions

Conceived the project: M.G., Y.L., H.L., Y.W., H.S. Designed experiments: M.G., Y.L., Y.S., T.L., D.D.N., M.W.M., L.H.D., I.R.-S., D.G., A.B., J.C., H.V., V.P., D.C.-R., Y.W., H.S. Performed experiments: M.G., Y.L., Y.S., T.L., D.D.N., M.W.M., L.H.D., I.R.-S., D.G., A.B., J.C., H.V., S.G., T.B.U., Y.W. Prepared samples: Y.S., T.L., D.D.N., M.W.M., L.H.D., R.I., I.R.-S., A.B., J.C., H.V., T.B.U., Y.W. Built instrumentation: T.L., H.V., Y.W. Developed and tested deep learning algorithms/software: Y.L., H.L., Y.W. Developed new registration and deconvolution algorithms/software: M.G., Y.L., P.L.R., Y.W. Recognized link between medical imaging algorithms and improved deconvolution: P.L.R. Tested new registration and deconvolution algorithms/software: M.G., W.A.M., Y.W. Developed and tested big data pipeline: M.G., Y.S., Y.W. Contributed lineaging/segmentation software and expertise: D.D.N., A.S., Z.B. Contributed samples: C.M.A., M.H., A.B.C. Wrote manuscript: M.G., Y.L., Y.S., P.L.R., Y.W., H.S. with input from all authors. All authors inspected data and contributed to the drafting of the manuscript. Supervised research: V.P., J.C.W., C.M.A., M.H., W.A.M., A.B.C., A.U., T.B.U., Z.B., D.C.-R. P.L.R., H.L., Y.W., H.S. Directed research: H.S.

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Correspondence to Huafeng Liu or Yicong Wu.

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Supplementary Information

Supplementary Figs. 1–20, Supplementary Tables 1–7 and Supplementary Notes 1–4

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Guo, M., Li, Y., Su, Y. et al. Rapid image deconvolution and multiview fusion for optical microscopy. Nat Biotechnol 38, 1337–1346 (2020). https://doi.org/10.1038/s41587-020-0560-x

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