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

You might also read

Related Articles

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

Sort by
Same author

The first pediatric emergency and critical care fellowship in sub-Saharan Africa: investing in local leaders to build subspecialty capacity in low- and middle-income countries.

Frontiers in pediatrics·2026
Same author

Comparative Analysis of Report-back of Research Results Strategies for Personal Chemical Exposure Data.

Journal of exposure science & environmental epidemiology·2026
Same author

Aggregation Methods for Quantifying PTM and Structural Changes in Bottom-Up Proteomics.

Journal of proteome research·2026
Same author

AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney.

Journal of proteome research·2026
Same author

Endophyte-induced systemic spatial reprogramming of metabolism in Populus trichocarpa roots under drought.

The New phytologist·2026
Same author

The impact of multiple long-term conditions on engaging with and maintaining behaviour change in older people with mild frailty: A qualitative study.

Journal of multimorbidity and comorbidity·2026
Same journal

Screen for Tissue-Specific Markers of Drug-Induced Phospholipidosis Using Mass Spectrometry Imaging.

Journal of the American Society for Mass Spectrometry·2026
Same journal

Molecular Framework Modification in Mass Spectrometry: Atom Exchange, Insertion, and Deletion.

Journal of the American Society for Mass Spectrometry·2026
Same journal

Interlaboratory Comparison of Orbitrap Analyzers in OrbiSIMS: Noise Parameters, Intensity Scaling, and Detection Limits.

Journal of the American Society for Mass Spectrometry·2026
Same journal

Isotope-Coded Derivatization Enables Accurate LC-MS/MS Quantification of Urinary THC-COOH without Isotope-Labeled Cannabinoid Standards.

Journal of the American Society for Mass Spectrometry·2026
Same journal

Characterization of Disulfide Bonds in Cyclic Peptides Using Electron Transfer-Ultraviolet Photodissociation Mass Spectrometry.

Journal of the American Society for Mass Spectrometry·2026
Same journal

Unveiling Ciprofloxacin Protonation Isomers: an Integrated Approach with Mass Spectrometry, Ion Mobility Spectrometry and Infrared Ion Spectroscopy.

Journal of the American Society for Mass Spectrometry·2026
See all related articles
  1. Home
  3. Unsupervised Semantic Segmentation Models For Region Of Interest Identification.
  1. Home
  3. Unsupervised Semantic Segmentation Models For Region Of Interest Identification.

Related Experiment Video

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

9.8K

Unsupervised Semantic Segmentation Models for Region of Interest Identification.

David J Degnan1,2, Bailey G Knight1, Logan A Lewis1

  • 1Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States.

Journal of the American Society for Mass Spectrometry
|April 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Eight unsupervised segmentation algorithms were compared for automated tissue region annotation. K-means and pytorch-tip showed best performance for spatial omics data, emphasizing model selection for accurate results.

Keywords:
medical imagingsegmentationsemantic segmentationunsupervised machine learning

More Related Videos

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

3.6K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.6K

Related Experiment Videos

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

9.8K
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

3.6K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.6K

Area of Science:

  • Computational pathology
  • Bioinformatics
  • Image analysis

Background:

  • Spatial omics technologies like mass spectrometry imaging (MSI) provide molecular distributions but require correlation with tissue morphology.
  • Manual annotation of tissue regions of interest (ROIs) for MSI data is time-consuming.
  • Automated segmentation models offer a time-saving alternative for annotating ROIs.

Purpose of the Study:

  • To compare the performance of eight unsupervised semantic segmentation algorithms for automated ROI annotation in MSI data.
  • To evaluate the impact of noise reduction techniques on segmentation accuracy.
  • To provide recommendations for selecting optimal segmentation models for spatial omics studies.

Main Methods:

  • Eight unsupervised semantic segmentation algorithms (R and Python) were applied to PAS-stained kidney and plant root images.
  • Manual annotations were used as ground truth for performance evaluation.
  • Dimension reduction techniques were tested for noise reduction.
  • Performance metrics (e.g., balanced accuracy, time) were calculated for each model.

    • Main Results:

    • K-means and pytorch-tip demonstrated the best performance in terms of balanced accuracy and processing time, particularly at smaller cluster sizes.
    • All algorithms showed decreased performance with increasing cluster numbers.
    • The choice of segmentation model significantly impacted downstream statistical analyses.

    Conclusions:

    • Unsupervised segmentation models can effectively automate tissue ROI annotation for spatial omics data.
    • Model selection is crucial and should be based on case-specific performance evaluation, as no single model excels in all scenarios.
    • K-means and pytorch-tip are recommended for specific applications requiring a balance of accuracy and speed.