Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Reporting transparency in veterinary pathology deep learning: A systematic review of reproducibility-critical details.

Veterinary pathology·2026

Same author

Data set creation for supervised deep learning-based analysis of microscopic images: Review of important considerations and recommendations.

Veterinary pathology·2026

Same author

A Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis.

Scientific data·2026

Same author

LBR and LAP2 mediate heterochromatin tethering to the nuclear periphery to preserve genome homeostasis.

Nature cell biology·2026

Same author

Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear Segmentation for Digital Microscopy Samples.

Sensors (Basel, Switzerland)·2026

Same author

A simplified co-culture reveals altered cardiotoxic responses to doxorubicin in hPSC-derived cardiomyocytes in the presence of endothelial cells.

Stem cell reports·2026

Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methods·2026

Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026

Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026

Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026

Same journal

Deep molecular profiling in three dimensions.

Nature methods·2026

Same journal

3D pathology-guided microdissection.

Nature methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 21, 2025

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

Segmentation metric misinterpretations in bioimage analysis.

Dominik Hirling^1,2, Ervin Tasnadi^1,2, Juan Caicedo³

¹Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged, Hungary.

|July 27, 2023

Summary

This summary is machine-generated.

Quantitative evaluation of bioimage analysis segmentation algorithms is often misinterpreted due to ambiguous metrics. This study clarifies metric definitions, revealing how misinterpretations impact competition results and proposing solutions.

More Related Videos

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

Related Experiment Videos

Last Updated: Jul 21, 2025

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

Area of Science:

Bioimage analysis
Computational biology
Image processing

Background:

Quantitative evaluation of image segmentation algorithms is critical in bioimage analysis.
Commonly used assessment scores are frequently misinterpreted, with multiple definitions sharing the same name.
This ambiguity can lead to inaccurate comparisons of algorithm performance.

Purpose of the Study:

To highlight the ambiguities in evaluation metrics for image segmentation algorithms.
To demonstrate how misinterpretations of these metrics can affect the outcomes of influential competitions.
To propose guidelines for addressing the existing problems with segmentation metric evaluation.

Main Methods:

Analysis of commonly used image segmentation evaluation metrics.
Review of existing literature and competition methodologies.
Identification of conflicting definitions and their implications.

Main Results:

Demonstration of how different definitions of the same metric yield varied results.
Evidence showing that metric misinterpretations can significantly alter algorithm rankings in competitions.
Identification of specific metrics prone to ambiguity.

Conclusions:

Standardization of metric definitions is essential for accurate bioimage analysis.
Clear guidelines are needed to prevent misinterpretation of segmentation algorithm performance.
Addressing metric ambiguity will improve the reliability and reproducibility of bioimage analysis research.