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

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 simul...

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Published inNature photonics Vol. 17; no. 5; pp. 442 - 450
Main Authors Zhou, Kevin C., Harfouche, Mark, Cooke, Colin L., Park, Jaehee, Konda, Pavan C., Kreiss, Lucas, Kim, Kanghyun, Jönsson, Joakim, Doman, Thomas, Reamey, Paul, Saliu, Veton, Cook, Clare B., Zheng, Maxwell, Bechtel, John P., Bègue, Aurélien, McCarroll, Matthew, Bagwell, Jennifer, Horstmeyer, Gregor, Bagnat, Michel, Horstmeyer, Roarke
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 01.05.2023
Nature Publishing Group
Subjects
631/1647/245/2226
631/1647/328
639/624/1075/1083
639/624/1107/328
639/624/1107/510
Algorithms
Applied and Technical Physics
Arrays
Cameras
Computer applications
Design optimization
Frames per second
Image acquisition
Image reconstruction
Larvae
Microscopy
Neural networks
Optical instruments
Optical properties
Organisms
Parallel processing
Physics
Physics and Astronomy
Quantum Physics
Redundancy
Spatial discrimination
Spatial resolution
Topography
Video
Zebrafish
parallelized microscopy
self-supervised learning
behavioral imaging
3D imaging
computational microscopy
camera array
Online AccessGet full text
ISSN1749-4885
1749-4893
1749-4893
DOI10.1038/s41566-023-01171-7

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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 cm 2 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. 3D-RAPID, a scalable computational microscope using 54 cameras, records 3D topographic videos of freely moving organisms over an area of 135 cm 2 at a spatial resolution of tens of micrometres and at a throughput exceeding 5 gigapixels per second.
AbstractList Wide field of view microscopy that can resolve 3D information at high speed and spatial resolution is highly desirable for studying the behaviour of freely moving model organisms. However, it is challenging to design an optical instrument that optimises all these properties simultaneously. Existing techniques typically require the acquisition of sequential image snapshots to observe large areas or measure 3D information, thus compromising on 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 an area of 135 cm2, achieving up to 230 frames per second at spatiotemporal throughputs 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 resulting 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 collections of several freely behaving organisms, including ants, fruit flies, and zebrafish larvae.
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 cm 2 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. 3D-RAPID, a scalable computational microscope using 54 cameras, records 3D topographic videos of freely moving organisms over an area of 135 cm 2 at a spatial resolution of tens of micrometres and at a throughput exceeding 5 gigapixels per second.
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.3D-RAPID, a scalable computational microscope using 54 cameras, records 3D topographic videos of freely moving organisms over an area of 135 cm2 at a spatial resolution of tens of micrometres and at a throughput exceeding 5 gigapixels per second.
Wide field of view microscopy that can resolve 3D information at high speed and spatial resolution is highly desirable for studying the behaviour of freely moving model organisms. However, it is challenging to design an optical instrument that optimises all these properties simultaneously. Existing techniques typically require the acquisition of sequential image snapshots to observe large areas or measure 3D information, thus compromising on 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 an area of 135 cm2, achieving up to 230 frames per second at spatiotemporal throughputs 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 resulting 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 collections of several freely behaving organisms, including ants, fruit flies, and zebrafish larvae.Wide field of view microscopy that can resolve 3D information at high speed and spatial resolution is highly desirable for studying the behaviour of freely moving model organisms. However, it is challenging to design an optical instrument that optimises all these properties simultaneously. Existing techniques typically require the acquisition of sequential image snapshots to observe large areas or measure 3D information, thus compromising on 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 an area of 135 cm2, achieving up to 230 frames per second at spatiotemporal throughputs 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 resulting 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 collections of several freely behaving organisms, including ants, fruit flies, and zebrafish larvae.
Author Bechtel, John P.
Zhou, Kevin C.
Bègue, Aurélien
Jönsson, Joakim
Doman, Thomas
Saliu, Veton
Bagnat, Michel
Konda, Pavan C.
Cook, Clare B.
Kim, Kanghyun
Horstmeyer, Roarke
Reamey, Paul
Zheng, Maxwell
Bagwell, Jennifer
Harfouche, Mark
McCarroll, Matthew
Cooke, Colin L.
Park, Jaehee
Kreiss, Lucas
Horstmeyer, Gregor
AuthorAffiliation 4 Department of Cell Biology, Duke University, Durham, NC 27710, USA
5 Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
3 Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
2 Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
1 Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
6 Current affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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Issue 5
Keywords parallelized microscopy
self-supervised learning
behavioral imaging
3D imaging
computational microscopy
camera array
Language English
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KCZ and RH conceived the idea and initiated the research. KCZ developed the algorithms and theory, with the help of CLC, JP, PCK, and RH. KCZ wrote the code for and performed 3D video reconstruction and stitching, animal tracking, and data analysis. MH, TD, PR, VS, CBC, MZ, and RH developed the MCAM hardware and acquisition software. KCZ acquired and analyzed the biological data, with the help of JPB, JB, AB, GH, and RH. MM, JB and MB provided input and supervision on biological experiments. TD and KCZ created the supplementary videos. KCZ wrote the manuscript and created the figures, with input from all authors. RH supervised the research.
Author contributions
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Snippet Wide-field-of-view microscopy that can resolve three-dimensional (3D) information at high speed and spatial resolution is particularly desirable for studying...
Wide field of view microscopy that can resolve 3D information at high speed and spatial resolution is highly desirable for studying the behaviour of freely...
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Algorithms
Applied and Technical Physics
Arrays
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Frames per second
Image acquisition
Image reconstruction
Larvae
Microscopy
Neural networks
Optical instruments
Optical properties
Organisms
Parallel processing
Physics
Physics and Astronomy
Quantum Physics
Redundancy
Spatial discrimination
Spatial resolution
Topography
Video
Zebrafish
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Title Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second
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