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

Artificial Intelligence-informed Architectural Insights of 3-dimensional Glandular Networks Identify Patients With Prostate Cancer at a Higher Risk of Biochemical Recurrence.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2026
Same author

Spatio-Temporal Characterization of Gastric Distensibility in Upper Endoscopy Identifies the Presence of Helicobacter pylori.

IEEE transactions on medical imaging·2026
Same author

Towards deep-learning based detection and quantification of intestinal metaplasia on digitized gastric biopsies: a multi-expert comparative study.

Scientific reports·2026
Same author

Multimodal educational model for the management of placenta accreta spectrum: Participants' perceived usefulness.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics·2026
Same author

Door-In-Door-Out Time Effect on Clinical Outcome According to Reperfusion Time in Endovascular Treatment.

Stroke (Hoboken, N.J.)·2026
Same author

Systematic characterization of mammalian extracellular vesicles using nano-flow cytometry.

Extracellular vesicle·2025
Same journal

Deep Learning Based Framework for Detection and Classification of Leukemia Using Microscopic Images.

Microscopy research and technique·2026
Same journal

Externally Controlled In Situ SEM: Multi-Rate Scanning With Signal Regulation and Spatiotemporal Fusion.

Microscopy research and technique·2026
Same journal

In Situ TEM Observation of Phase Transformation Nucleation at the Near-Surface of Synthetic Aragonite.

Microscopy research and technique·2026
Same journal

Morpho-Anatomical and HPTLC Investigations of Lysimachia nummularia L. (Primulaceae) Grown in Switzerland.

Microscopy research and technique·2026
Same journal

Macroscopic, Histological and Ultrastructural Features of the Tongue of the Anatolian Wild Boar (Sus scrofa libycus).

Microscopy research and technique·2026
Same journal

Ultrastructural Insights Into the Reproductive Anatomy and Eggs of Cotton Pink Bollworm, Pectinophora gossypiella Saunders (Lepidoptera: Gelechiidae).

Microscopy research and technique·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Micro-structural tissue analysis for automatic histopathological image annotation.

Gloria Díaz1, Eduardo Romero

  • 1Bioingenium Research Group, Faculty of Medicine, National University of Colombia, Bogotá, Colombia.

Microscopy Research and Technique
|October 15, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for identifying biological concepts in digital histopathology images. The approach accurately annotates and locates multiple tissue concepts, improving diagnostic insights.

More Related Videos

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Related Experiment Videos

Last Updated: May 28, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Area of Science:

  • Digital Pathology
  • Computational Biology
  • Medical Image Analysis

Background:

  • Histopathological images are crucial for disease diagnosis.
  • Automated analysis of these images can enhance diagnostic accuracy and efficiency.
  • Extracting high-level semantic concepts remains a challenge.

Purpose of the Study:

  • To develop and validate a new computational approach for extracting high-level semantic concepts from digital histopathological images.
  • To enable both annotation and coarse localization of biological concepts within these images.
  • To improve the understanding of tissue structures through automated image analysis.

Main Methods:

  • The approach involves five key steps: stain decomposition (hematoxylin and eosin), color standardization, part-based image representation using local patches, a discriminative classification model, and block-based annotation.
  • Stain decomposition separates dye contributions for robust feature extraction.
  • A part-based representation and discriminative classification model link image patterns to biological concepts.

Main Results:

  • The method was evaluated on 655 skin histopathology images, identifying 10 biological concepts.
  • The approach achieved a sensitivity of 84% and a specificity of 67% in annotating images with multiple concepts.
  • The strategy successfully provided both annotation and coarse localization of biological concepts.

Conclusions:

  • The proposed method offers a robust framework for semantic concept extraction in digital histopathology.
  • This approach has the potential to aid pathologists in diagnosing diseases by providing detailed image annotations.
  • Further development could lead to more precise localization and a wider range of identifiable biological concepts.