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: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.7K
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...
4.7K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

887
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...
887
Uncertainty: Overview00:59

Uncertainty: Overview

979
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.
979
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.1K
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...
1.1K

You might also read

Related Articles

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

Sort by
Same author

DiffGeo-AOR: Diffusion-Optimized Medical Grading via Geometric Priors enhanced Autoregressive Ordinal Regression.

IEEE transactions on medical imaging·2026
Same author

Real-World Insights in Designing SteatoStat: An End-to-End Deep Learning Pipeline for Hepatic Steatosis Quantification.

Diagnostics (Basel, Switzerland)·2026
Same author

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026
Same author

STAGE challenge: Structural-Functional Transition in Glaucoma Assessment.

Medical image analysis·2026
Same author

Angiography-free diagnosis of retinal diseases via interpretable multi-modal learning.

NPJ digital medicine·2026
Same author

Multi-Granularity Topological Reasoning for Anatomically Consistent Vasculature Parsing.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Related Experiment Video

Updated: Sep 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Online Bayesian Approximation Based Uncertainty Aware Model for Ophthalmic Image Segmentation.

Yinglin Zhang, Risa Higashita, Lingxi Zeng

    IEEE Journal of Biomedical and Health Informatics
    |July 31, 2025
    PubMed
    Summary

    This study introduces the Online Bayesian approximation based Uncertainty-aware Network (OBU-Net) for improved ophthalmic image segmentation. OBU-Net enhances segmentation accuracy and reliability by addressing ambiguity in medical images.

    More Related Videos

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    42.8K
    Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
    05:49

    Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

    Published on: November 1, 2024

    966

    Related Experiment Videos

    Last Updated: Sep 13, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.9K
    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    42.8K
    Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
    05:49

    Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

    Published on: November 1, 2024

    966

    Area of Science:

    • Medical image analysis
    • Artificial intelligence in healthcare
    • Computer vision

    Background:

    • Robust segmentation of multimodal ophthalmic images is difficult due to low contrast, size/shape variations, and disease interference.
    • Assessing the reliability of artificial intelligence (AI) is critical for clinical adoption in medical imaging.

    Purpose of the Study:

    • To propose a novel deep learning network, the Online Bayesian approximation based Uncertainty-aware Network (OBU-Net), for robust ophthalmic image segmentation.
    • To enhance the reliability and accuracy of AI-driven segmentation in clinical settings.

    Main Methods:

    • Developed an efficient online Bayesian method to continuously update a spatial uncertainty map during training.
    • Introduced the Spatial Uncertainty Aware Block (SUA-B) to leverage uncertainty maps for focusing on ambiguous regions.
    • Integrated hierarchical predictions by extracting pixel-wise confidence from multi-scale outputs.

    Main Results:

    • OBU-Net achieved superior performance compared to state-of-the-art methods across six diverse datasets and multiple segmentation tasks.
    • Metamorphic testing confirmed the algorithm's stability against random perturbations, demonstrating robustness.
    • An image-level uncertainty score was proposed and validated for effective evaluation of segmentation reliability.

    Conclusions:

    • OBU-Net offers a robust and reliable solution for ophthalmic image segmentation across various modalities.
    • The proposed uncertainty quantification methods enhance the trustworthiness of AI models in clinical applications.
    • This work advances the development of dependable AI tools for medical image analysis.