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

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

6.0K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
6.0K

You might also read

Related Articles

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

Sort by
Same author

Learning from single timestamps: complexity estimation in laparoscopic cholecystectomy.

International journal of computer assisted radiology and surgery·2026
Same author

Algorithm-driven, phenotype-directed bioactive molecular discovery.

Communications chemistry·2026
Same author

mirtronDB 2.0: enhanced database with novel mirtron discoveries.

Bioinformatics (Oxford, England)·2026
Same author

Ex vivo human placental imaging: Navigating modalities, scales, and analysis approaches to obtain fit-for-purpose data.

Placenta·2026
Same author

From mice to rhinos: Whole-organ quantification of 3D mammalian placental structure using correlative multiscale imaging.

Placenta·2026
Same author

Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge.

Medical image analysis·2026

Related Experiment Video

Updated: Sep 26, 2025

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.9K

SuRVoS 2: Accelerating Annotation and Segmentation for Large Volumetric Bioimage Workflows Across Modalities and

Avery Pennington1, Oliver N F King1, Win Min Tun1

  • 1Diamond Light Source Ltd., Didcot, United Kingdom.

Frontiers in Cell and Developmental Biology
|April 18, 2022
PubMed
Summary

The SuRVoS application now accelerates volumetric imaging data analysis using machine learning-based annotation and segmentation. This updated tool supports large datasets and correlative imaging, streamlining complex segmentation tasks.

Keywords:
U-netX-ray microscopy imagingannotationcomputer visionopen source softwarepython (programming language)segmentation (image processing)volume electron microscopy (vEM)

More Related Videos

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.7K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.0K

Related Experiment Videos

Last Updated: Sep 26, 2025

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.9K
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.7K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.0K

Area of Science:

  • * Computational Biology
  • * Biomedical Imaging
  • * Machine Learning

Background:

  • * Volumetric imaging generates large datasets, making data analysis, particularly annotation and segmentation, a significant bottleneck.
  • * Manual annotation and segmentation are time-consuming and labor-intensive processes crucial for interpreting volumetric imaging data.
  • * Existing tools often lack the scalability and flexibility required for modern, large-scale imaging projects.

Purpose of the Study:

  • * To redesign and update the SuRVoS application to address the data analysis bottleneck in volumetric imaging.
  • * To integrate both manual and machine learning-based segmentation and annotation techniques.
  • * To enhance support for large datasets, correlative imaging, and cloud-based processing.

Main Methods:

  • * Implemented a client-server architecture enabling processing of large datasets on high-performance servers with GPUs.
  • * Incorporated supervoxel-based methods to accelerate segmentation painting and model training.
  • * Developed a Pytorch-based server for flexible implementation of deep learning segmentation modules and cloud compatibility.
  • * Integrated the client-side application with Napari for seamless incorporation into open-source image analysis workflows.

Main Results:

  • * SuRVoS now supports manual and machine learning-based segmentation and annotation, including crowd-sourced data.
  • * Layer support allows simultaneous viewing and annotation of multiple datasets, facilitating correlative data use.
  • * The new architecture enables efficient processing of large volumetric and correlative imaging datasets.
  • * Enhanced flexibility and extensibility through a plugin-based server architecture.

Conclusions:

  • * The updated SuRVoS application significantly accelerates annotation and segmentation of volumetric imaging data.
  • * It provides a scalable and flexible platform for diverse imaging modalities and scales.
  • * Integration with Napari and cloud computing enhances its utility in open-source and distributed research environments.