High-speed large-scale 4D activities mapping of moving C. elegans by deep-learning-enabled light-field microscopy on a chip

Zhu, Tingting; Zhu, Lanxin; Li, Yang; Chen, Xiaopeng; He, Mingyang; Li, Guo; Zhang, Hao; Gao, Shangbang; Peng, Fei

doi:10.1016/j.snb.2021.130638

Cited by 12 publications

(11 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also anticipate more combinations of DL algorithms and high-content analysis. A “self-learning microscope” system is expected, which would combine automation, high speed, high throughput, and high repeatability, and could help biologists collect a large amount of living cell data and extract reliable and accurate scientific results [ 25 , 124 ]. Another shortcoming of organoid systems is the lack of communication between tissues.…”

Section: Discussionmentioning

confidence: 99%

“…The traditional method does not offer a good solution [ 121 – 123 ]. Therefore, Zhu et al [ 124 ] proposed a new fusion of microfluidics and light-field microscopes to achieve high-speed four-dimensional (4D, space–time) imaging of moving nematodes on a chip. The combination of light-field microscopy (LFM) that supports DL and chip-based sample manipulation can continuously record the 3D instantaneous position of nematodes and screen a large number of worms on a high-throughput chip (Fig.…”

Section: Deep Learning In Organoid Images and Potential Integrationsmentioning

confidence: 99%

“…a 3D trajectories and speeds of six moving wild-type worms (1–6) and six uncoordinated-type mutant worms (7–12) over 450 ms (adapted and modified from Ref. [ 124 ], Copyright 2021, with permission from Elsevier B.V.). b Image restoration procedure based on DL (adapted and modified from Ref.…”

Section: Deep Learning In Organoid Images and Potential Integrationsmentioning

confidence: 99%

See 2 more Smart Citations

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Chen

et al. 2023

Bio-des. Manuf.

View full text Add to dashboard Cite

In modern terminology, “organoids” refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions. Graphic abstract

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Deep Learning In Organoid Images and Potential Integrationsmentioning

confidence: 99%

Section: Deep Learning In Organoid Images and Potential Integrationsmentioning

confidence: 99%

See 1 more Smart Citation

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Chen

et al. 2023

Bio-des. Manuf.

View full text Add to dashboard Cite

show abstract

“… 4 https://imagej.nih.gov/ij/` LFDisplay The Board of Trustees of The Leland Stanford Junior University http://graphics.stanford.edu/software/LFDisplay/ Other Confocal microscope Olympus FV3000 Leica SP8-STED/FLIM/FCS Commercial fluorescence microscope Olympus BX51 Objective Olympus LUMPlanFLN ×40/NA 0.8 water Nikon Fluor ×20/0.5 water Olympus UPLSAPO 40X2 ×40/NA 0.95 Leica HC PL APO CS2 ×20/NA 0.75 Oil Relay system Nikon AF 60 mm 2.8D Relay lens Thorlabs AC508-080-A Camera Hamamatsu Flash 4.0 V2 Microlens array OKO Optics APO-Q-P150-F3.5 (633) Objective scanner Physik Instrumente (PI) P-725.4CD Mirror Thorlabs PF20-03-P01 Optomechanical components Thorlabs KCB2C LCP01T AC508-080 CH1060 RS6P Microfluidic chamber Zhu et al. 5 N/A …”

Section: Key Resources Tablementioning

confidence: 99%

A practical guide to deep-learning light-field microscopy for 3D imaging of biological dynamics

Zhu

Peng

2023

STAR Protocols

Self Cite

View full text Add to dashboard Cite

“…The incorporating of deep neural networks enlarges this design space of microscopy by introducing prior knowledge of high-resolution data 20,21 . Previous view-channel-depth light-field microscopy (VCD-LFM) is capable of directly reconstructing high-resolution 3D volume from 2D light-field (LF) raw data by splitting various views from LF and incorporating successive extracted features in network into multiple channels to yield 3D image stacks, successfully pushing the spatial resolution of LFM to diffraction limit and showing outstanding performance in imaging cellular structures [22][23][24] . However, learning the mapping function to reconstruct from 2D under-sampling LF to 3D super-resolution (SR) volumes is challenging for usual one-stage model, since the degradation model of light field imaging is an extremely intricate process coupling multiple resolution degradation and noise and compressing the space bandwidth product by ~500 times, resulting in the limited performance of reaching sub-diffraction-limited resolution with high fidelity.…”

Section: Introductionmentioning

confidence: 99%

Sustained 3D isotropic imaging of subcellular dynamics using adaptive VCD light-field microscopy 2.0

Zhu

Sun

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Long-term and high-speed 3D imaging of live cells beyond diffraction limit remains a big challenge for current super-resolution microscopy implementations, owing to their severe phototoxicity and limited volumetric imaging rate. While emerging light-field microscopy (LFM) has mitigated this issue through rapid and mild 3D imaging of dynamic biological processes with single 2D snapshots, it suffers from a suboptimal spatial resolution close to diffraction limit, which greatly compromises its applications for live-cell imaging. Here, we propose a super-resolution light-field reconstruction strategy based on an optics-aware view-channel-depth (VCD 2.0) deep-learning strategy. Through a stepwise inversion of the physical degradation process during light-filed imaging, VCD 2.0 strongly pushes the light-field microscopy beyond diffraction limit, and achieves instant 3D reconstructions from raw 2D light-field snapshots with an ~14-fold resolution improvement. After being combined with a simple-and-robust light-field add-on design, VCD 2.0 further enables long-term 3D super-resolution imaging (over 30000 volumes) of various intracellular dynamics (FOV of ~220*220*10 micro m3) even on an ordinary 2D fluorescence microscope. Using this novel and readily-accessible approach, we successfully capture the fast morphology changes of diverse organelles, such as the extension and constriction in lysosome, at a near isotropic resolution of ~120 nm, and a high volume rate up to 50 Hz.

show abstract

High-speed large-scale 4D activities mapping of moving C. elegans by deep-learning-enabled light-field microscopy on a chip

Cited by 12 publications

References 48 publications

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

A practical guide to deep-learning light-field microscopy for 3D imaging of biological dynamics

Sustained 3D isotropic imaging of subcellular dynamics using adaptive VCD light-field microscopy 2.0

Contact Info

Product

Resources

About