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

You might also read

Related Articles

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

Sort by
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

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 author

Nonlinear Gradient Field Estimation in Diffusion MRI Tensor Simulation.

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

Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI.

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

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.

Proceedings of SPIE--the International Society for Optical Engineering·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

721

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

Ryan Datteri1, Andrew J Asman1, Bennett A Landman1

  • 1Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.

Proceedings of Spie--The International Society for Optical Engineering
|December 29, 2025
PubMed
Summary
This summary is machine-generated.

A new method, AQUIRC, improves medical image segmentation accuracy by estimating registration errors locally. This technique enhances atlas selection for better anatomical and functional information transfer in multi-atlas segmentation.

Keywords:
Image registrationatlas-based segmentationnon-rigid registrationregistration circuitsregistration error

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

43.4K
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

3.3K

Related Experiment Videos

Last Updated: Jan 7, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

721
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

43.4K
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

3.3K

Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Image Segmentation

Background:

  • Multi-atlas registration-based segmentation is crucial for transferring anatomical and functional information in medical imaging.
  • Segmentation accuracy heavily relies on the quality of non-rigid registration between atlases and target images.
  • Existing methods for atlas selection and segmentation combination have limitations, especially in challenging registration scenarios.

Purpose of the Study:

  • To evaluate the efficacy of AQUIRC (Anatomical QUality and Image Registration Confidence) for local atlas selection in multi-atlas segmentation.
  • To assess AQUIRC's performance in improving segmentation accuracy for difficult non-rigid registration cases.
  • To compare AQUIRC's performance against established segmentation combination techniques.

Main Methods:

  • AQUIRC was applied for local error estimation in non-rigid registration.
  • The method was tested on six anatomical structures: brainstem, optic chiasm, optic nerves (left/right), and eyes (left/right).
  • Results were compared with Majority Vote, STAPLE, Non-Local STAPLE, and Locally-Weighted Vote segmentation techniques.

Main Results:

  • AQUIRC demonstrated effectiveness in selecting appropriate atlases at a local level.
  • The method improved the accuracy of projected information onto target images.
  • AQUIRC's performance was found to be comparable to state-of-the-art multi-atlas segmentation methods.

Conclusions:

  • AQUIRC serves as a robust method for combining segmentations and enhancing accuracy in multi-atlas registration.
  • The technique shows promise for improving segmentation quality in challenging medical imaging applications.
  • AQUIRC offers a valuable tool for advancing the field of medical image analysis and segmentation.