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Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second

Nature Photonics volume 17, pages 442–450 (2023)Cite this article

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Abstract

Wide-field-of-view microscopy that can resolve three-dimensional (3D) information at high speed and spatial resolution is particularly desirable for studying the behaviour of freely moving organisms. However, it is challenging to design an optical instrument that optimizes all these properties simultaneously. Existing techniques typically require the acquisition of sequential image snapshots to observe large areas or measure 3D information, thus compromising speed and throughput. Here we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over a 135 cm2 area, achieving up to 230 frames per second at a spatiotemporal throughput exceeding 5 gigapixels per second. 3D-RAPID employs a 3D reconstruction algorithm that, for each synchronized snapshot, fuses all 54 images into a composite that includes a co-registered 3D height map. The self-supervised 3D reconstruction algorithm trains a neural network to map raw photometric images to 3D topography using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. The reconstruction process is thus robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. We demonstrate the broad applicability of 3D-RAPID with several collections of freely behaving organisms: ants, fruit flies and zebrafish larvae.

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Fig. 1: Overview of 3D-RAPID.
Fig. 2: Computational 3D reconstruction and stitching algorithm for 3D-RAPID.
Fig. 3: Zebrafish larvae (10 dpf) swimming in an open arena with interspersed microcapsule food particles (AP100), acquired at 60 f.p.s. for 10 s (Supplementary Videos 1 and 3).
Fig. 4: Adult fruit flies freely moving across a flat, noise-patterned surface, acquired at 60 f.p.s. for 8 s (Supplementary Videos 8 and 10).
Fig. 5: Harvester ants freely moving across a flat, noise-patterned surface, acquired at 60 f.p.s. for 10 s (Supplementary Videos 11 and 12).

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Data availability

The data that support the findings of this study are available from the Duke Research Data Repository at https://doi.org/10.7924/r4db86b1q. Interactive, full-resolution, reconstructed video frames can be viewed at https://gigazoom.rc.duke.edu/.

Code availability

The Python code used to generate the high-resolution 3D videos featured in this study is available at https://github.com/kevinczhou/3D-RAPID.

References

  1. Bellen, H. J., Tong, C. & Tsuda, H. 100 years of Drosophila research and its impact on vertebrate neuroscience: a history lesson for the future. Nat. Rev. Neurosci. 11, 514–522 (2010).

    Article  Google Scholar 

  2. Oliveira, R. F. Mind the fish: zebrafish as a model in cognitive social neuroscience. Front. Neural Circuits 7, 131 (2013).

    Article  Google Scholar 

  3. Kalueff, A. V., Stewart, A. M. & Gerlai, R. Zebrafish as an emerging model for studying complex brain disorders. Trends Pharmacol. Sci. 35, 63–75 (2014).

    Article  Google Scholar 

  4. Dreosti, E., Lopes, G., Kampff, A. R. & Wilson, S. W. Development of social behavior in young zebrafish. Front. Neural Circuits 9, 39 (2015).

    Article  Google Scholar 

  5. Pandey, U. B. & Nichols, C. D. Human disease models in Drosophila melanogaster and the role of the fly in therapeutic drug discovery. Pharmacol. Rev. 63, 411–436 (2011).

    Article  Google Scholar 

  6. Sakai, C., Ijaz, S. & Hoffman, E. J. Zebrafish models of neurodevelopmental disorders: past, present, and future. Front. Mol. Neurosci. 11, 294 (2018).

    Article  Google Scholar 

  7. MacRae, C. A. & Peterson, R. T. Zebrafish as tools for drug discovery. Nat. Rev. Drug Discov. 14, 721–731 (2015).

    Article  Google Scholar 

  8. Maitra, U. & Ciesla, L. Using Drosophila as a platform for drug discovery from natural products in Parkinson’s disease. Medchemcomm 10, 867–879 (2019).

    Article  Google Scholar 

  9. Hirsch, H. V. et al. Behavioral effects of chronic exposure to low levels of lead in Drosophila melanogaster. Neurotoxicology 24, 435–442 (2003).

    Article  Google Scholar 

  10. Bambino, K. & Chu, J. Zebrafish in toxicology and environmental health. Curr. Top. Dev. Biol. 124, 331–367 (2017).

    Article  Google Scholar 

  11. Rihel, J. et al. Zebrafish behavioral profiling links drugs to biological targets and rest/wake regulation. Science 327, 348–351 (2010).

    Article  ADS  Google Scholar 

  12. McCarroll, M. N. et al. Zebrafish behavioural profiling identifies GABA and serotonin receptor ligands related to sedation and paradoxical excitation. Nat. Commun. 10, 4078 (2019).

    Article  ADS  Google Scholar 

  13. Mathias, J. R., Saxena, M. T. & Mumm, J. S. Advances in zebrafish chemical screening technologies. Future Med. Chem. 4, 1811–1822 (2012).

    Article  Google Scholar 

  14. Wright, D. & Krause, J. Repeated measures of shoaling tendency in zebrafish (Danio rerio) and other small teleost fishes. Nat. Protoc. 1, 1828–1831 (2006).

    Article  Google Scholar 

  15. Harpaz, R., Nguyen, M. N., Bahl, A. & Engert, F. Precise visuomotor transformations underlying collective behavior in larval zebrafish. Nat. Commun. 12, 6578 (2021).

    Article  ADS  Google Scholar 

  16. Dankert, H., Wang, L., Hoopfer, E. D., Anderson, D. J. & Perona, P. Automated monitoring and analysis of social behavior in Drosophila. Nat. Methods 6, 297–303 (2009).

    Article  Google Scholar 

  17. Robie, A. A., Seagraves, K. M., Egnor, S. R. & Branson, K. Machine vision methods for analyzing social interactions. J. Exp. Biol. 220, 25–34 (2017).

    Article  Google Scholar 

  18. Dunn, T. W. et al. Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion. Elife 5, 12741 (2016).

    Article  Google Scholar 

  19. Johnson, R. E. et al. Probabilistic models of larval zebrafish behavior reveal structure on many scales. Curr. Biol. 30, 70–82 (2020).

    Article  Google Scholar 

  20. Bianco, I. H., Kampff, A. R. & Engert, F. Prey capture behavior evoked by simple visual stimuli in larval zebrafish. Front. Syst. Neurosci. 5, 101 (2011).

    Article  Google Scholar 

  21. Patterson, B. W., Abraham, A. O., MacIver, M. A. & McLean, D. L. Visually guided gradation of prey capture movements in larval zebrafish. J. Exp. Biol. 216, 3071–3083 (2013).

    Google Scholar 

  22. Muto, A. & Kawakami, K. Prey capture in zebrafish larvae serves as a model to study cognitive functions. Front. Neural Circuits 7, 110 (2013).

    Article  Google Scholar 

  23. Bolton, A. D. et al. Elements of a stochastic 3D prediction engine in larval zebrafish prey capture. Elife 8, 51975 (2019).

    Article  Google Scholar 

  24. Lohmann, A. W. Scaling laws for lens systems. Appl. Opt. 28, 4996–4998 (1989).

    Article  ADS  Google Scholar 

  25. Park, J., Brady, D. J., Zheng, G., Tian, L. & Gao, L. Review of bio-optical imaging systems with a high space–bandwidth product. Adv. Photonics 3, 044001 (2021).

    Article  Google Scholar 

  26. Zheng, G., Horstmeyer, R. & Yang, C. Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics 7, 739–745 (2013).

    Article  ADS  Google Scholar 

  27. Kumar, N., Gupta, R. & Gupta, S. Whole slide imaging (WSI) in pathology: current perspectives and future directions. J. Digit. Imaging 33, 1034–1040 (2020).

    Article  Google Scholar 

  28. Borowsky, A. D. et al. Digital whole slide imaging compared with light microscopy for primary diagnosis in surgical pathology a multicenter, double-blinded, randomized study of 2045 cases. Arch. Pathol. Lab. Med. 144, 1245–1253 (2020).

    Article  Google Scholar 

  29. Grover, D., Katsuki, T. & Greenspan, R. J. Flyception: imaging brain activity in freely walking fruit flies. Nat. Methods 13, 569–572 (2016).

    Article  Google Scholar 

  30. Ehrlich, D. E. & Schoppik, D. Control of movement initiation underlies the development of balance. Curr. Biol. 27, 334–344 (2017).

    Article  Google Scholar 

  31. Ehrlich, D. E. & Schoppik, D. A primal role for the vestibular sense in the development of coordinated locomotion. Elife 8, 45839 (2019).

    Article  Google Scholar 

  32. Akitake, B. et al. Coordination and fine motor control depend on Drosophila TRPγ. Nat. Commun. 6, 7288 (2015).

    Article  ADS  Google Scholar 

  33. Shamble, P. S., Hoy, R. R., Cohen, I. & Beatus, T. Walking like an ant: a quantitative and experimental approach to understanding locomotor mimicry in the jumping spider Myrmarachne formicaria. Proc. R. Soc. B 284, 20170308 (2017).

    Article  Google Scholar 

  34. Günel, S. et al. DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila. Elife 8, 48571 (2019).

    Article  Google Scholar 

  35. Lobato-Rios, V. et al. NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster. Nat. Methods 19, 620–627 (2022).

    Article  Google Scholar 

  36. Wolf, E. Three-dimensional structure determination of semi-transparent objects from holographic data. Opt. Commun. 1, 153–156 (1969).

    Article  ADS  Google Scholar 

  37. Chowdhury, S. et al. High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images. Optica 6, 1211–1219 (2019).

    Article  ADS  Google Scholar 

  38. Chen, B.-C. et al. Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution. Science 346, 1257998 (2014).

    Article  Google Scholar 

  39. Patel, K. B. et al. High-speed light-sheet microscopy for the in-situ acquisition of volumetric histological images of living tissue. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-022-00849-7 (2022).

  40. Huang, D. et al. Optical coherence tomography. Science 254, 1178–1181 (1991).

    Article  ADS  Google Scholar 

  41. Zhou, K. C., Qian, R., Dhalla, A.-H., Farsiu, S. & Izatt, J. A. Unified k-space theory of optical coherence tomography. Adv. Opt. Photonics 13, 462–514 (2021).

    Article  ADS  Google Scholar 

  42. Zhou, K. C. et al. Computational 3D microscopy with optical coherence refraction tomography. Optica 9, 593–601 (2022).

    Article  ADS  MathSciNet  Google Scholar 

  43. Wilburn, B. et al. High performance imaging using large camera arrays. ACM Trans. Graph. 24, 765–776 (2005).

    Article  Google Scholar 

  44. Brady, D. J. et al. Multiscale gigapixel photography. Nature 486, 386–389 (2012).

    Article  ADS  Google Scholar 

  45. Lin, X., Wu, J., Zheng, G. & Dai, Q. Camera array based light field microscopy. Biomed. Opt. Express 6, 3179–3189 (2015).

    Article  Google Scholar 

  46. Fan, J. et al. Video-rate imaging of biological dynamics at centimetre scale and micrometre resolution. Nat. Photonics 13, 809–816 (2019).

    Article  ADS  Google Scholar 

  47. Thomson, E. E. et al. Gigapixel imaging with a novel multi-camera array microscope. Elife 11, e74988 (2022).

  48. Jiang, Y., Karpf, S. & Jalali, B. Time-stretch LiDAR as a spectrally scanned time-of-flight ranging camera. Nat. Photonics 14, 14–18 (2020).

    Article  Google Scholar 

  49. Riemensberger, J. et al. Massively parallel coherent laser ranging using a soliton microcomb. Nature 581, 164–170 (2020).

    Article  ADS  Google Scholar 

  50. Rogers, C. et al. A universal 3D imaging sensor on a silicon photonics platform. Nature 590, 256–261 (2021).

    Article  ADS  Google Scholar 

  51. Qian, R. et al. Video-rate high-precision time-frequency multiplexed 3D coherent ranging. Nat. Commun. 13, 1476 (2022).

    Article  ADS  Google Scholar 

  52. Geng, J. Structured-light 3D surface imaging: a tutorial. Adv. Opt. Photonics 3, 128–160 (2011).

    Article  ADS  Google Scholar 

  53. Aguilar, J.-J., Torres, F. & Lope, M. Stereo vision for 3D measurement: accuracy analysis, calibration and industrial applications. Measurement 18, 193–200 (1996).

    Article  ADS  Google Scholar 

  54. Scharstein, D. & Szeliski, R. High-accuracy stereo depth maps using structured light. In Proc. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 195–202 (IEEE, 2003).

  55. Harfouche, M. et al. Imaging across multiple spatial scales with the multi-camera array microscope. Optica 10, (2023); https://doi.org/10.1364/OPTICA.478010

  56. Kumar, R., Anandan, P. & Hanna, K. Direct recovery of shape from multiple views: a parallax based approach. In Proc. 12th International Conference on Pattern Recognition 685–688 (IEEE, 1994).

  57. Zhou, K. C. et al. Mesoscopic photogrammetry with an unstabilized phone camera. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 7535–7545 (IEEE, 2021).

  58. Ulyanov, D., Vedaldi, A. & Lempitsky, V. Deep image prior. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 9446–9454 (IEEE, 2018).

  59. Zollikofer, C. Stepping patterns in ants – influence of speed and curvature. J. Exp. Biol. 192, 95–106 (1994).

    Article  Google Scholar 

  60. Reinhardt, L. & Blickhan, R. Level locomotion in wood ants: evidence for grounded running. J. Exp. Biol. 217, 2358–2370 (2014).

    Google Scholar 

  61. Westerfield, M. The Zebrafish Book: a Guide for the Laboratory Use of Zebrafish (Danio rerio) 4th edn (Univ. Oregon Press, 2000); https://zfin.org/zf_info/zfbook/zfbk.html

Download references

Acknowledgements

We would like to thank K. Branson, S. Turaga, T. Dunn, A. Chakraborty and M. Hoffmann for their helpful feedback on the manuscript. Research reported in this publication was supported by the Office of Research Infrastructure Programs (ORIP), Office of the Director, National Institutes of Health of the National Institutes of Health and the National Institute of Environmental Health Sciences (NIEHS) of the National Institutes of Health under award number R44OD024879 (M.H., J.P., T.D., P.R., V.S., M.Z., J.P.B., A.B. and G.H.), the National Cancer Institute (NCI) of the National Institutes of Health under award number R44CA250877 (M.H., J.P., T.D., P.R., V.S., M.Z., J.P.B., A.B. and G.H.), the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under award number R43EB030979 (M.H., J.P., T.D., P.R., V.S., M.Z., J.P.B., A.B. and G.H.), the National Science Foundation under award number 2036439 (M.H., J.P., T.D., P.R., V.S., M.Z., J.P.B., A.B. and G.H.) and the Duke Coulter Translational Partnership Award (K.C.Z., K.K. and R.H.).

Author information

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  1. Kevin C. Zhou

    Present address: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA

Authors and Affiliations

  1. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Kevin C. Zhou, Pavan C. Konda, Lucas Kreiss, Kanghyun Kim, Joakim Jönsson, Clare B. Cook & Roarke Horstmeyer

  2. Ramona Optics Inc., Durham, NC, USA

    Kevin C. Zhou, Mark Harfouche, Jaehee Park, Thomas Doman, Paul Reamey, Veton Saliu, Clare B. Cook, Maxwell Zheng, John P. Bechtel, Aurélien Bègue, Gregor Horstmeyer & Roarke Horstmeyer

  3. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

    Colin L. Cooke & Roarke Horstmeyer

  4. Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA

    Matthew McCarroll

  5. Department of Cell Biology, Duke University, Durham, NC, USA

    Jennifer Bagwell & Michel Bagnat

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Contributions

K.C.Z. and R.H. conceived the idea and initiated the research. With the help of C.L.C., J.P., P.C.K. and R.H., K.C.Z. developed the algorithms and theory. K.C.Z. wrote the code for and performed 3D video reconstruction and stitching, animal tracking and data analysis. M.H., T.D., P.R., V.S., C.B.C., M.Z. and R.H. developed the MCAM hardware and acquisition software. With the help of J.P.B., J.B., A.B., G.H., K.K. and R.H., K.C.Z. acquired and analysed the biological data. M.M., J.B. and M.B. provided input to and supervision of the biological experiments. T.D., J.J., K.K. and K.C.Z. created the supplementary videos and visualizations. K.C.Z. wrote the manuscript and created the figures, with input from all authors. K.C.Z, L.K. and R.H. revised the manuscript. R.H. supervised the research.

Corresponding authors

Correspondence to Kevin C. Zhou or Roarke Horstmeyer.

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Competing interests

R.H. and M.H. are cofounders of Ramona Optics, Inc., which is commercializing multi-camera array microscopes. M.H., J.P., T.D., P.R., V.S., C.B.C., M.Z., J.P.B. and G.H. are or were employed by Ramona Optics, Inc. during the course of this research. K.C.Z. is a consultant for Ramona Optics, Inc. The remaining authors declare no competing interests.

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Nature Photonics thanks Chao Zuo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Population-level analysis of the zebrafish larvae featured in Fig. 3 and Supplementary Videos 1,3.

a Fish head height vs. elevation angle for all 40 fish over time. Lines define the approximate physical limits due to geometric fish mobility constraints. b Kernel density estimates of the height distributions of the zebrafish and AP100 food particles. Eye vergence vs. head height (c) and vs. elevation angle (d) plots are color-coded by the maximum height the fish attained in the 10-sec video. Fixed effect components of the linear mixed-effects regression lines are plotted (p = 0.33 and p < 10−5) for c and d, respectively.

Extended Data Fig. 2 Population-level analysis of the adult fruit flies featured in Fig. 4 and Supplementary Videos 8,10.

The six plots show kernel densities of the heights of the head, thorax, and abdomen for various behaviors. Differences of head (p < 10−7), thorax (p < 10−16), and abdomen (p < 10−62) heights across behaviors are statistically significant (n = 43 flies).

Supplementary information

Supplementary Information

Supplementary Figs. 1–5, Table 1, Equations 1–13 and Sections 1–7.

Supplementary Video 1

60 f.p.s., 36.6 MP video of freely swimming zebrafish larvae (10 dpf) feeding on mostly floating AP100 food particles. The left panel is the photometric composite and the right panel is the 3D height map. The video zooms into three feeding events (or attempts) by two different fish.

Supplementary Video 2

230 f.p.s., 9.1 MP video of freely swimming zebrafish larvae (10 dpf) feeding on mostly floating AP100 food particles. The left panel is the photometric composite and the right panel is the 3D height map. The video zooms in on three independent feeding events by three different fish. The third fish can be seen swallowing the food particle.

Supplementary Video 3

60 f.p.s., 36.6 MP video of freely swimming zebrafish larvae (10 dpf) feeding on mostly floating AP100 food particles. The left panel shows the full FOV with the trajectories mapped out. The panels on the right each correspond to individual fish, uniquely identified via a two-digit number, whose position and orientation are denoted with red annotations. The border colours of the right-hand panels non-uniquely match those of the tracks in the left-hand panel, to assist the viewer in matching the fish to the trajectories. Right-hand panels appear and disappear when the fish enters or exits the FOV. The first half of the video shows the photometric values, and the second half of the video shows the 3D height maps.

Supplementary Video 4

60 f.p.s., 36.6 MP video of 20 dpf zebrafish larvae feeding on live brine shrimp. The left panel is the photometric composite and the right panel is the 3D height map. The video zooms in on two feeding events from two different fish.

Supplementary Video 5

230 f.p.s., 9.1 MP video of 20 dpf zebrafish larvae feeding on live brine shrimp. The left panel is the photometric composite and the right panel is the 3D height map. The video zooms in on one feeding event.

Supplementary Video 6

60 f.p.s., 36.6 MP video of a large school of 5 dpf zebrafish larvae freely swimming in an open arena at high speed. The left panel is the photometric composite and the right panel is the 3D height map.

Supplementary Video 7

230 f.p.s., 9.1 MP video of a large school of 5 dpf zebrafish larvae freely swimming in an open arena at high speed. The left panel is the photometric composite and the right panel is the 3D height map.

Supplementary Video 8

60 f.p.s., 36.6 MP video of freely moving fruit flies. The left panel is the photometric composite and the right panel is the 3D height map.

Supplementary Video 9

230 f.p.s., 9.1 MP video of freely moving fruit flies. The left panel is the photometric composite and the right panel is the 3D height map.

Supplementary Video 10

60 f.p.s., 36.6 MP video of freely moving fruit flies. The left panel shows the full FOV with the trajectories mapped out. The panels on the right each correspond to individual flies, uniquely identified via a two-digit number, whose position is denoted by a red circle. The border colours of the right-hand panels non-uniquely match those of the tracks in the left-hand panel, to assist the viewer in matching the flies to the trajectories. Right-hand panels appear and disappear when the fish enters or exits the FOV. The first half of the video shows the photometric values, and the second half of the video shows the 3D height maps.

Supplementary Video 11

60 f.p.s., 36.6 MP video of freely moving harvester ants. The left panel is the photometric composite and the right panel is the 3D height map.

Supplementary Video 12

230 f.p.s., 9.1 MP video of freely moving harvester ants. The left panel is the photometric composite and the right panel is the 3D height map.

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Zhou, K.C., Harfouche, M., Cooke, C.L. et al. Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second. Nat. Photon. 17, 442–450 (2023). https://doi.org/10.1038/s41566-023-01171-7

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