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

Elevated IL-6 receptor expression on CD4+ T cells contributes to the increased Th17 responses in patients with chronic hepatitis B.

Virology journal·2011
Same author

Neurochemical plasticity of nitric oxide synthase isoforms in neurogenic detrusor overactivity after spinal cord injury.

Neurochemical research·2011
Same author

[Clinical significance of 5-HT and DA levels in serum and cerebrospinal fluid of the patients with delayed encephalopathy after acute carbon monoxide poisoning].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2011
Same author

Reconstitution of lysosomal NAADP-TRP-ML1 signaling pathway and its function in TRP-ML1(-/-) cells.

American journal of physiology. Cell physiology·2011
Same author

[The association between HBV genotyping and clinical characteristics and expression of TH1/TH2 cytokines].

Zhonghua shi yan he lin chuang bing du xue za zhi = Zhonghua shiyan he linchuang bingduxue zazhi = Chinese journal of experimental and clinical virology·2011
Same author

Bis[5-(2-pyrid-yl)pyrazine-2-carbonitrile]-silver(I) tetra-fluorido-borate.

Acta crystallographica. Section E, Structure reports online·2011

Related Experiment Video

Updated: Oct 18, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

568

An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images.

Shaodi Yang1, Yuqian Zhao1,2,3, Miao Liao4

  • 1School of Automation, Central South University, Changsha 410083, China.

Sensors (Basel, Switzerland)
|September 28, 2021
PubMed
Summary

This study introduces an improved unsupervised learning framework for 3D abdominal CT image registration, enhancing multi-organ alignment accuracy. The novel method ensures accurate spatial consistency for clinical applications.

Keywords:
abdominal CTconvolutional neural networkmedical imageregistration

More Related Videos

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

741
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.2K

Related Experiment Videos

Last Updated: Oct 18, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

568
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

741
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.2K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Accurate spatial alignment of medical images is crucial for diagnosis and treatment planning.
  • Existing medical image registration methods face challenges in accuracy and efficiency, particularly for complex 3D datasets.

Purpose of the Study:

  • To propose an improved unsupervised learning-based framework for multi-organ registration of 3D abdominal CT images.
  • To enhance the accuracy and robustness of medical image registration using deep learning techniques.

Main Methods:

  • A novel framework integrating coarse-to-fine recursive cascaded network (RCN) modules within a U-net architecture.
  • Implementation of a topology-preserving loss function to maintain the integrity of the transformation field.
  • Validation using four public medical image databases.

Main Results:

  • The proposed method demonstrated superior performance compared to traditional and existing deep learning-based registration techniques.
  • Achieved accurate multi-organ registration results on 3D abdominal CT images.
  • The method shows promise for real-time and high-precision clinical registration requirements.

Conclusions:

  • The developed unsupervised learning framework significantly improves 3D abdominal CT image registration.
  • The integration of RCN modules and topology-preserving loss enhances accuracy and prevents transformation field distortion.
  • The method is a promising advancement for clinical applications requiring precise medical image alignment.