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

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

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: May 18, 2026

Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

Finding Seeds for Segmentation Using Statistical Fusion.

Fangxu Xing1, Andrew J Asman, Jerry L Prince

  • 1Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA 21218.

Proceedings of Spie--The International Society for Optical Engineering
|September 29, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic method for placing seeds in medical images using multi-atlas registration and statistical fusion. This approach enhances segmentation accuracy and efficiency for large datasets.

More Related Videos

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

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Published on: August 23, 2017

Related Experiment Videos

Last Updated: May 18, 2026

Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 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

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Published on: August 23, 2017

Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Image Processing

Background:

  • Accurate medical image analysis relies on precise image labeling.
  • Manual seed placement for segmentation algorithms is time-consuming and limits scalability.
  • Existing semi-automatic methods require human intervention, posing challenges for large datasets.

Purpose of the Study:

  • To develop an automated algorithm for seed placement in medical image analysis.
  • To improve the efficiency and accuracy of image segmentation initialization.
  • To overcome the limitations of manual seed placement in large-scale medical imaging studies.

Main Methods:

  • Proposed an automatic seed placement algorithm utilizing multi-atlas registration and statistical fusion.
  • Deformably registered neuroanatomical object centers of mass across a training set of atlases.
  • Incorporated transformation biases into a continuous Simultaneous Truth And Performance Level Estimation (STAPLE) fusion method.

Main Results:

  • Demonstrated improved estimation accuracy by incorporating registration biases into STAPLE fusion.
  • Showcased reduced fusion error compared to single registration strategies.
  • Validated the technique on real 3D brain MR image atlases, correcting data bias.

Conclusions:

  • The proposed multi-atlas registration and STAPLE fusion method effectively automates seed placement for medical image segmentation.
  • This technique significantly reduces data bias and fusion error, enhancing quantitative analysis.
  • Offers a scalable and efficient solution for initializing segmentation algorithms in large medical imaging datasets.