Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

10.6K
This is a method for training a multi-slice U-Net for multi-class segmentation of cryo-electron tomograms using a portion of one tomogram as a training input. We describe how to infer this network to other tomograms and how to extract segmentations for further analyses, such as subtomogram averaging and filament...
10.6K
Deep Neural Networks for Image-Based Dietary Assessment13:19

Deep Neural Networks for Image-Based Dietary Assessment

9.9K
The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image...
9.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

3.3K
An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised...
3.3K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

13.0K
We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell...
13.0K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

12.2K
We describe a novel methodology for creating naturalistic 3-D objects and object categories with precisely defined feature variations. We use simulations of the biological processes of morphogenesis and phylogenesis to create novel, naturalistic virtual 3-D objects and object categories that can then be rendered as visual images or haptic...
12.2K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

3.5K
A high-speed and open-top ultraviolet photoacoustic microscope that can provide histological images intraoperatively for surgical margin analysis is demonstrated, including the system configuration, optical alignment, sample preparation, and experimental...
3.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Construction and validation of a predictive nomogram model for invasive fungal infections in sepsis patients with severe pneumonia in the ICU.

Frontiers in cellular and infection microbiology·2026
Same author

Correction to "Multienzyme Active Nanozyme for Efficient Sepsis Therapy through Modulating Immune and Inflammation Inhibition".

ACS applied materials & interfaces·2026
Same author

Marginal Contrast Saturation-Based Intraoperative Scoring System Predicts Objective Response to DEB-TACE: Development and Validation of a CBCT-Derived Real-Time Score.

Cancer medicine·2026
Same author

Predicting preoperative axillary lymph node metastasis to guide surgical decisions in invasive breast cancer.

Frontiers in oncology·2026
Same author

Compartmentalized co-cultivation and temporal transcriptomics reveal SexM-mediated crosstalk between pheromone signaling and metabolic reprogramming in the industrial carotenoid producer Blakeslea trispora.

Genomics·2026
Same author

Quality and flavor chemistry evaluation of Sichuan Gongfu Black Tea with different leaf tenderness levels.

Food research international (Ottawa, Ont.)·2026

Related Experiment Video

Updated: Jan 19, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.6K

Object-independent image-based wavefront sensing approach using phase diversity images and deep learning.

Qi Xin, Guohao Ju, Chunyue Zhang

    Optics Express
    |September 13, 2019
    PubMed
    Summary

    This study introduces a novel deep learning method for image-based wavefront sensing, accurately reconstructing phase aberrations from extended scenes without prior scene data. The approach utilizes a deep long short-term memory (LSTM) network for precise phase recovery.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K

    Related Experiment Videos

    Last Updated: Jan 19, 2026

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

    10.6K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K

    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Image Processing

    Background:

    • Traditional wavefront sensing methods often struggle with extended scenes or require extensive training data.
    • Accurate phase aberration retrieval is crucial for high-resolution imaging and optical system correction.

    Purpose of the Study:

    • To develop a robust and versatile image-based wavefront sensing technique using deep learning.
    • To enable phase aberration correction for both point sources and extended scenes simultaneously.
    • To train the model without relying on simulated or real extended scene data.

    Main Methods:

    • A novel feature extraction method in the frequency domain, independent of object information, is employed.
    • A deep long short-term memory (LSTM) network is utilized to map extracted features to phase aberrations.
    • Phase diversity images are used for feature extraction, enabling non-linear mapping.

    Main Results:

    • The proposed deep learning approach demonstrates high accuracy and effectiveness in simulations and experiments.
    • The deep LSTM network shows superior non-linear fitting capacity compared to Resnet 18 for phase aberration problems.
    • The influence of light incoherency on wavefront phase recovery accuracy is quantitatively analyzed.

    Conclusions:

    • The developed image-based wavefront sensing method offers a significant advancement for optical system correction.
    • This work highlights the potential of deep learning, particularly LSTM networks, in advanced optical metrology.
    • The findings pave the way for improved high-resolution image reconstruction in various imaging applications.