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

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
F Distribution01:19

F Distribution

The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...

You might also read

Related Articles

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

Sort by
Same author

Applying the Algorithm "Assessing Quality Using Image Registration Circuits" (AQUIRC) to Multi-Atlas Segmentation.

Proceedings of SPIE--the International Society for Optical Engineering·2025
Same author

Nucleus subtype classification using inter-modality learning.

Proceedings of SPIE--the International Society for Optical Engineering·2024
Same author

Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation.

Proceedings of SPIE--the International Society for Optical Engineering·2024
Same author

Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale.

PLoS biology·2024
Same author

Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water-fat separated MRI.

Journal of medical imaging (Bellingham, Wash.)·2024
Same author

Functional correlation tensors in brain white matter and the effects of normal aging.

Brain imaging and behavior·2024
Same journal

UniOCTSeg++: Refined Hierarchical Prompt Strategy and Bi-directional Progressive Consistency Learning for Universal Retinal Layer Segmentation in OCT.

IEEE transactions on medical imaging·2026
Same journal

Volumetric Functional Ultrasound Imaging in Macaques.

IEEE transactions on medical imaging·2026
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Videos

Formulating spatially varying performance in the statistical fusion framework.

Andrew J Asman1, Bennett A Landman

  • 1Department of Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA. andrew.j.asman@vanderbilt.edu

IEEE Transactions on Medical Imaging
|March 23, 2012
PubMed
Summary
This summary is machine-generated.

Spatial STAPLE enhances label fusion by modeling rater performance variation across space. This novel approach improves accuracy in image segmentation tasks using simulated and real-world data.

Related Experiment Videos

Area of Science:

  • Medical image analysis
  • Computational biology
  • Machine learning

Background:

  • Current label fusion methods use global or voxelwise models.
  • Statistical fusion optimality depends on accurate rater error models.
  • Existing models often focus on extreme performance scenarios.

Purpose of the Study:

  • To extend the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm.
  • To introduce a method accounting for spatially varying rater performance.
  • To improve the accuracy of automated image segmentation.

Main Methods:

  • Developed Spatial STAPLE, an extension of the STAPLE algorithm.
  • Incorporated a smooth, voxelwise performance level field unique to each rater.
  • Evaluated performance using simulated and empirical datasets.

Main Results:

  • Spatial STAPLE demonstrated significant improvements over existing label fusion techniques.
  • The method effectively models spatially varying rater performance.
  • Achieved higher accuracy in label fusion tasks compared to state-of-the-art methods.

Conclusions:

  • Spatial STAPLE offers a more robust framework for label fusion.
  • This approach enhances the reliability of automated image segmentation.
  • The method provides a significant advancement in statistical image analysis.