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

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

327
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
327
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.1K
Structural Classification of Joints01:20

Structural Classification of Joints

7.9K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
7.9K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.4K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.4K

You might also read

Related Articles

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

Sort by
Same author

Toward real-time alignment of 3D CT and 2D X-ray with multi-stage CNNs.

Computer assisted surgery (Abingdon, England)·2026
Same author

Study of French inter-expert variability in thyroid nodule ultrasound.

European thyroid journal·2026
Same author

Managing Adhesive Small Bowel Obstruction: Immediate Risks and Long-Term Burden in France.

Annals of surgery·2026
Same author

Anatomic Characterisation of the Vascular System of the Lower Limb using Artificial Intelligence Based Segmentation Models.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery·2026
Same author

Analysis of Clinical Improvement at 90 Days After Varicose Vein Surgery Using Machine Learning.

Angiology·2026
Same author

Automatic Segmentation of Intraluminal Thrombus in Abdominal Aortic Aneurysms Based on CT Images: A Comprehensive Review of Deep Learning-Based Methods.

Journal of clinical medicine·2025
Same journal

LiftReg: Limited Angle 2D/3D Deformable Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Inverse Consistency by Construction for Multistep Deep Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

uniGradICON: A Foundation Model for Medical Image Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
See all related articles

Related Experiment Video

Updated: Apr 21, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

509

Sparse Bayesian registration.

Loïc Le Folgoc, Hervé Delingette, Antonio Criminisi

    Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    |October 22, 2014
    PubMed
    Summary
    This summary is machine-generated.

    We developed a Sparse Bayesian framework for non-rigid image registration. This method efficiently models complex deformations and automatically estimates model parameters, achieving state-of-the-art results in cardiac image analysis.

    More Related Videos

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    2.0K
    Profiling Maternal Behavior Responses During Whole-Brain Imaging
    07:12

    Profiling Maternal Behavior Responses During Whole-Brain Imaging

    Published on: January 24, 2025

    1.5K

    Related Experiment Videos

    Last Updated: Apr 21, 2026

    Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
    07:34

    Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

    Published on: November 7, 2025

    509
    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    2.0K
    Profiling Maternal Behavior Responses During Whole-Brain Imaging
    07:12

    Profiling Maternal Behavior Responses During Whole-Brain Imaging

    Published on: January 24, 2025

    1.5K

    Area of Science:

    • Medical image analysis
    • Computational anatomy
    • Biomedical engineering

    Background:

    • Non-rigid registration is crucial for analyzing medical images.
    • Current methods face challenges in automatic parameter estimation and model flexibility.
    • Accurate motion and strain analysis in cardiac imaging is clinically significant.

    Purpose of the Study:

    • To introduce a novel Sparse Bayesian framework for non-rigid image registration.
    • To address limitations in current registration techniques, particularly automatic parameter estimation.
    • To evaluate the framework's performance on cardiac imaging datasets.

    Main Methods:

    • A principled Sparse Bayesian framework was developed.
    • The approach employs a flexible, sparse model for deformation representation.
    • It enables automatic joint estimation of noise and regularization parameters.

    Main Results:

    • The framework demonstrated feasibility and strong performance on cine MR, tagged MR, and 3D US cardiac images.
    • State-of-the-art results were achieved on benchmark datasets.
    • Accurate evaluation of cardiac motion and strain was shown.

    Conclusions:

    • The proposed Sparse Bayesian framework offers an effective solution for non-rigid registration.
    • It provides a flexible and principled approach with automatic parameter estimation.
    • The method shows significant potential for advancing quantitative analysis in cardiac imaging.