PUBLICATION
Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning
- Authors
- Wang, Z., Zhu, L., Zhang, H., Li, G., Yi, C., Li, Y., Yang, Y., Ding, Y., Zhen, M., Gao, S., Hsiai, T.K., Fei, P.
- ID
- ZDB-PUB-210213-14
- Date
- 2021
- Source
- Nature Methods 18(5): 551-556 (Journal)
- Registered Authors
- Keywords
- none
- MeSH Terms
-
- Animals
- Behavior, Animal
- Biomechanical Phenomena
- Caenorhabditis elegans/physiology*
- Deep Learning*
- Heart/physiology*
- Image Processing, Computer-Assisted/methods*
- Microscopy/methods*
- Motor Activity/physiology
- Neurons/physiology
- Zebrafish
- PubMed
- 33574612 Full text @ Nat. Methods
Citation
Wang, Z., Zhu, L., Zhang, H., Li, G., Yi, C., Li, Y., Yang, Y., Ding, Y., Zhen, M., Gao, S., Hsiai, T.K., Fei, P. (2021) Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning. Nature Methods. 18(5):551-556.
Abstract
Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artifacts, nonuniform resolution and a slow reconstruction speed have limited its full capabilities for in toto extraction of dynamic spatiotemporal patterns in samples. Here, we combined a view-channel-depth (VCD) neural network with light-field microscopy to mitigate these limitations, yielding artifact-free three-dimensional image sequences with uniform spatial resolution and high-video-rate reconstruction throughput. We imaged neuronal activities across moving Caenorhabditis elegans and blood flow in a beating zebrafish heart at single-cell resolution with volumetric imaging rates up to 200 Hz.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping