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To study the behavior of freely moving model organisms such as zebrafish and fruit flies across multiple spatial scales, it would be ideal to use a light microscope that can resolve 3D information over a wide field of view (FOV) at high speed and high spatial resolution. However, it is challenging to design an optical instrument to achieve all of these properties simultaneously. Existing techniques for large-FOV microscopic imaging and for 3D image measurement typically require many sequential image snapshots, 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 throughputs exceeding 5 gigapixels (GPs) per second. 3D-RAPID features a 3D reconstruction algorithm that, for each synchronized temporal snapshot, simultaneously fuses all 54 images seamlessly into a globally-consistent composite that includes a coregistered 3D height map. The self-supervised 3D reconstruction algorithm itself trains a spatiotemporally-compressed convolutional neural network (CNN) that maps raw photometric images to 3D topography, using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. As a result, our end-to-end 3D reconstruction algorithm is robust to generalization errors and scales to arbitrarily long microscope array video. We demonstrate the broad applicability of our high-throughput computational 3D microscope on several freely moving organisms, including ants, fruit flies, and zebrafish larvae.
This repository contains raw files for high-throughput, synchronized, multi-view video of populations of three organisms (zebrafish larvae, fruit flies, and harvester ants), freely moving through a >130 cm^2 field of view. These files can be used to computationally reconstruct high-throughput 3D video using our new technique, termed 3D-RAPID. For more information about our method, please see our accompanying publication: "Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second" (https://doi.org/10.1038/s41566-023-01171-7).
The files are organized into three folders by organism ("fruit_flies", "zebrafish", "harvester_ants"), each containing the raw video ("raw_video.nc") and a calibration file ("calibration_data.nc"). The raw video files are about 50 GB each, comprising 54 synchronized videos of freely behaving organisms, captured at ~5 GB/sec for 10 seconds. The calibration file is a single snapshot from 54 cameras of a flat patterned target, for calibrating the camera parameters. To this end, the "camera_calibration_initial_guess.mat" file is also included to initialize the camera calibration step. For detailed instructions on how to process these files, please see the accompanying 3D-RAPID Github repository (https://github.com/kevinczhou/3D-RAPID), which contains the 3D reconstruction python code.... [Read More]
Total Size
8 files (155 GB)
Data Citation
Zhou, K., Harfouche, M., Cooke, C. L., Park, J., Konda, P. C., Kreiss, L., Doman, J., Reamey, P., Saliu, V., Cook, C., Zheng, M., Bechtel, J. P., McCarroll, M., Bagwell, J., Horstmeyer, G., Bagnat, M. & Horstmeyer, R. (2023). Data from: Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second. Duke Research Data Repository. https://doi.org/10.7924/r4db86b1q
Zhou, K.C., Harfouche, M., Cooke, C.L. et al. (2023). Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second. Nature Photonics 17, 442–450. https://doi.org/10.1038/s41566-023-01171-7
