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

TL;DR

This paper introduces 3D-RAPID, a high-speed, large-area 3D microscopy system using an array of 54 cameras and a neural network-based reconstruction algorithm, enabling detailed behavioral imaging of freely moving organisms at gigapixel per second rates.

Contribution

The paper presents a novel synchronized camera array combined with a self-supervised CNN for real-time 3D imaging of large biological samples, overcoming previous speed and scale limitations.

Findings

Achieves 230 fps over 135 cm^2 area at 5 GPs/sec.

Successfully captures 3D behavior of ants, fruit flies, and zebrafish.

Robust, scalable 3D reconstruction with neural network training.

Abstract

To study the behavior of freely moving model organisms such as zebrafish (Danio rerio) and fruit flies (Drosophila) 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-cm^2 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 videos from arbitrarily sized camera arrays. The scalable hardware and software design of 3D-RAPID addresses a longstanding problem in the field of behavioral imaging, enabling parallelized 3D observation of large collections of freely moving organisms at high spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex barbatus), fruit flies, and zebrafish larvae.