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

Uncertainty: Overview00:59

Uncertainty: Overview

1.4K
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.
1.4K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

9.9K
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...
9.9K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.2K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.2K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

394
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
394
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
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...
1.6K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

99.1K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
99.1K

You might also read

Related Articles

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

Sort by
Same author

Postoperative cerebral oxygen availability and neurodevelopment in children with d-transposition of the great arteries.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Predicting facial deformation during respiratory mask fitting with semi-supervised graph neural networks.

Medical engineering & physics·2026
Same author

Automatic Segmentation of the Left Ventricle Through the Cardiac Cycle in Pediatric Echocardiography Videos Using SegFormer Architecture.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Evaluation of Respiratory Mask Fitting Using Finite Element Analysis Numerical Simulations and Experimental Pressure Measurements.

Annals of biomedical engineering·2025
Same author

Proceedings of the 15<sup>th</sup> International Newborn Brain Conference: Long-term outcome studies, developmental care, palliative care, ethical dilemmas, and challenging clinical scenarios in neonatal neurology: Fota Island, Cork, Ireland, February 28<sup>th</sup> - March 2<sup>nd</sup> 2024.

Journal of neonatal-perinatal medicine·2025
Same author

Transcatheter correction of the scimitar variant with dual pulmonary venous drainage-an international multicentre series.

Cardiology in the young·2025

Related Experiment Video

Updated: Dec 30, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

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

Published on: August 13, 2014

24.9K

Epistemic Uncertainty Modeling for Vessel Segmentation.

Remi Martin, Joaquim Miro, Luc Duong

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary

    This study introduces a new method for segmenting multiple arteries in X-ray angiograms, improving accuracy and providing uncertainty maps. This enhances cardiovascular intervention analysis and data processing for tasks like 3D reconstruction.

    More Related Videos

    A Full Skin Defect Model to Evaluate Vascularization of Biomaterials In Vivo
    07:56

    A Full Skin Defect Model to Evaluate Vascularization of Biomaterials In Vivo

    Published on: August 28, 2014

    12.7K
    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
    06:18

    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

    Published on: April 5, 2024

    1.5K

    Related Experiment Videos

    Last Updated: Dec 30, 2025

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

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

    Published on: August 13, 2014

    24.9K
    A Full Skin Defect Model to Evaluate Vascularization of Biomaterials In Vivo
    07:56

    A Full Skin Defect Model to Evaluate Vascularization of Biomaterials In Vivo

    Published on: August 28, 2014

    12.7K
    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
    06:18

    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

    Published on: April 5, 2024

    1.5K

    Area of Science:

    • Medical Imaging
    • Cardiovascular Interventions
    • Artificial Intelligence in Medicine

    Background:

    • X-ray angiograms are the standard for cardiovascular interventions but are challenging to analyze due to contrast issues and artifacts.
    • Accurate multi-artery segmentation is crucial for improving cardiologist analysis and enabling advanced applications like 3D reconstruction and motion tracking.

    Purpose of the Study:

    • To develop a general and accurate method for multi-artery segmentation in X-ray angiograms.
    • To provide uncertainty quantification for the segmentation to guide expert review and data augmentation.

    Main Methods:

    • A novel multi-artery segmentation method was developed and validated on clinical X-ray angiogram data.
    • The method incorporates epistemic uncertainty mapping to identify areas requiring expert validation.

    Main Results:

    • The proposed method achieved an average segmentation accuracy of 94.9% on clinical data.
    • Demonstrated successful transfer learning across different artery types.
    • Epistemic uncertainty maps effectively highlighted regions needing expert review.

    Conclusions:

    • The developed segmentation method significantly improves the analysis of X-ray angiograms.
    • Uncertainty maps are valuable for ensuring segmentation reliability and identifying areas for further data improvement.