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

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

You might also read

Related Articles

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

Sort by

Same author

Role of Anode Composition and Electrolyte Interactions on the Thermo-Electrochemical Stability of Sodium-Ion Batteries.

ACS applied materials & interfaces·2026

Same author

Void Formation and Evolution Dynamics for Lithium Metal and Solid Electrolyte Interfaces.

ACS applied materials & interfaces·2026

Same author

Passivation-Induced Species Dynamics and Microstructural Evolution in Solid-State Lithium-Sulfur Cathodes.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Linking Pressure to Electrochemical Evolution in Solid-State Conversion Cathode Composites.

ACS applied materials & interfaces·2025

Same author

Mechanistic Understanding of Thermal Stability and Safety in Lithium Metal Batteries.

Chemical reviews·2025

Same author

Probing the Impact of Vacancy Diffusion on Void Dynamics at the Lithium Metal-Solid Electrolyte Interface.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Search research articles

Related Experiment Video

Updated: Oct 20, 2025

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

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

Published on: August 13, 2014

Quantifying the unknown impact of segmentation uncertainty on image-based simulations.

Michael C Krygier¹, Tyler LaBonte^2,3, Carianne Martinez²

¹Engineering Sciences Center, Sandia National Laboratories, Albuquerque, NM, USA.

Nature Communications

|September 15, 2021

Summary

Image segmentation uncertainty impacts physics simulations. This study introduces a framework to quantify this uncertainty, improving the credibility of image-based simulations by revealing hidden variations.

More Related Videos

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Related Experiment Videos

Last Updated: Oct 20, 2025

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

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

Published on: August 13, 2014

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Computational physics
Medical imaging analysis
Scientific simulation

Background:

Image-based simulations use 3D images for physical quantity calculations, requiring image segmentation for geometry creation.
Image segmentation introduces uncertainty due to variations from different manual and machine-learning tools, affecting simulation accuracy.

Purpose of the Study:

To demonstrate how image segmentation uncertainty propagates into physics simulations and compromises results.
To propose a general framework for rapidly quantifying segmentation uncertainty in image-based simulations.

Main Methods:

Developing a framework using segmentation uncertainty probability maps to systematically create uncertainty distributions.
Sampling these probability maps to analyze the propagation of segmentation variations into physics quantities.

Main Results:

Segmentation variations demonstrably compromise physics quantities in simulations.
The proposed framework quantifies segmentation uncertainty, revealing that uncertainty distributions can be non-normal and complex.
Established that bounding segmentation uncertainty may fail in complex simulations.

Conclusions:

Image segmentation uncertainty is a significant, often unrecognized factor in image-based simulations.
The developed framework enhances simulation credibility by quantifying and visualizing segmentation uncertainty.
While not eliminating uncertainty, this work provides a method to manage and understand its impact on simulation outcomes.