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

PCR-free cascaded CRISPR-Cas13a colorimetric platform (CLAMP) for non-invasive miRNA-based allergen-specific subtyping of allergic rhinitis.

Biosensors & bioelectronics·2026
Same author

MAGE-A in triple-negative breast cancer: molecular biology, epigenetic targeting, and immunotherapy.

Epigenomics·2025
Same author

Enhanced HoVerNet Optimization for Precise Nuclei Segmentation in Diffuse Large B-Cell Lymphoma.

Diagnostics (Basel, Switzerland)·2025
Same author

Prognostic Implications of MDM2 and CDK4 Co-amplification in Liposarcoma: Insights from FISH analysis for Translational Oncology.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2025
Same author

Global, regional, and national mortality of larynx cancer from 1990 to 2021: results from the global burden of disease study.

World journal of surgical oncology·2025
Same author

<i>Pereskia bleo</i> augments NK cell cytotoxicity against triple-negative breast cancer cells (MDA-MB-231).

PeerJ·2024

Related Experiment Video

Updated: Jan 17, 2026

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

625

Automated quantification and feature extraction of nuclei in diffuse large B-cell lymphoma using advanced imaging

Chee Chin Lim1,2, Gei Ki Tang1, Faezahtul Arbaeyah Hussain3,4

  • 1Faculty of Electronic Engineering and Technology, Universiti Malaysia Perlis, Arau, Perlis, Malaysia.

Biomedical Physics & Engineering Express
|September 15, 2025
PubMed
Summary

Accurate Diffuse Large B-Cell Lymphoma diagnosis is challenging. This study segmented H&E-stained slides, finding nuclear area differences in MYC-positive, MYC-negative, and normal samples, aiding subtyping.

Keywords:
H&E Staining Analysisautomated cell segmentationdata statistical analysisdiffuse large B-cell lymphoma (DLBCL)non-hodgkin lymphoma (NHL)nuclei feature extraction

More Related Videos

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

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

Published on: November 19, 2018

13.0K
Enhancing Tumor Content through Tumor Macrodissection
10:04

Enhancing Tumor Content through Tumor Macrodissection

Published on: February 12, 2022

12.1K

Related Experiment Videos

Last Updated: Jan 17, 2026

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

625
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

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

Published on: November 19, 2018

13.0K
Enhancing Tumor Content through Tumor Macrodissection
10:04

Enhancing Tumor Content through Tumor Macrodissection

Published on: February 12, 2022

12.1K

Area of Science:

  • Pathology
  • Computational Biology
  • Medical Imaging

Background:

  • Diffuse Large B-Cell Lymphoma (DLBCL) is a common non-Hodgkin lymphoma subtype.
  • Accurate DLBCL diagnosis and subtyping are complex, requiring expert analysis.
  • Global incidence of DLBCL necessitates improved diagnostic tools.

Purpose of the Study:

  • To develop image segmentation and classification methods for DLBCL slides.
  • To differentiate MYC-positive, MYC-negative, and normal DLBCL subtypes using morphological features.
  • To evaluate the utility of quantitative image analysis in DLBCL subtyping.

Main Methods:

  • Utilized a dataset of 108 H&E-stained DLBCL slide images.
  • Applied colour deconvolution for nuclei highlighting and watershed algorithm for segmentation.
  • Extracted nuclear morphological features (area, perimeter, diameter, circularity) and colour features (LAB, RGB spaces).

Main Results:

  • Significant differences (P < 0.05) were observed in nuclear area among DLBCL groups.
  • No significant differences were found for nuclear perimeter, diameter, or circularity.
  • Significant differences in colour features (Std Dev L, Std Dev RGB) were detected, but not in mean colour values.

Conclusions:

  • Nuclear area is a significant morphological feature for differentiating DLBCL subtypes.
  • Quantitative colour analysis provides additional discriminatory information.
  • Automated image analysis shows promise for improving DLBCL diagnosis and subtyping.