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

ortho–para-Directing Activators: –CH3, –OH, –⁠NH2, –OCH301:11

ortho–para-Directing Activators: –CH3, –OH, –⁠NH2, –OCH3

7.6K
All ortho–para directors, excluding halogens, are activating groups. These groups donate electrons to the ring, making the ring carbons electron-rich. Consequently, the reactivity of the aromatic ring towards electrophilic substitution increases. For instance, the nitration of anisole is about 10,000 times faster than the nitration of benzene. The electron-donating effect of the methoxy group in anisole activates the ortho and para positions on the ring and stabilizes the corresponding...
7.6K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

14.7K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
14.7K
Three Developmental Domains01:29

Three Developmental Domains

1.2K
Human development is typically examined across three main domains: physical, cognitive, and socio-emotional. These domains represent the significant areas of change and continuity throughout the lifespan, from infancy to late adulthood.
Physical Development
Physical processes, also known as maturation, encompass the biological changes that occur across an individual's life. These changes begin with genetic inheritance and continue through various stages, including growth in height and weight,...
1.2K
Membrane Domains01:18

Membrane Domains

7.3K
The membrane domains concentrate specific lipids and proteins at one place within the membrane, which helps in cell signaling, adhesion, and other critical cellular processes. These domains can differ in size, composition, function, and lifespan.
Protein Domains
The membrane comprises a group of distinct proteins responsible for carrying out a cell's specific function. For example, the plasma membrane of the human sperm, or a single germ cell, contains a unique set of proteins in the...
7.3K
Three-Domain System of Life01:21

Three-Domain System of Life

1.5K
Ribosomal RNA (rRNA) sequence analysis revealed three distinct groups of cells: eukaryotes, bacteria, and archaea. In 1978, Carl R. Woese proposed the concept of domains, a taxonomic level above kingdoms, to differentiate these groups. He suggested that archaea and bacteria, despite their similar appearance, represent separate domains. Domains differ in rRNA, membrane lipid structure, transfer RNA, and antibiotic sensitivity.In this classification, animals, plants, and fungi belong to the...
1.5K
Conservation of Protein Domains02:26

Conservation of Protein Domains

4.2K
4.2K

You might also read

Related Articles

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

Sort by
Same author

Bacterial mRNA Vaccines: Programming Immunity Against Antimicrobial Resistance.

Drug development research·2026
Same author

Personalizing adjuvant radiotherapy decisions in breast cancer: an externally test MRI radiomics approach for recurrence risk stratification.

Radiation oncology (London, England)·2026
Same author

Melatonin Alleviates Graft Biliary Fibrosis by Inhibiting VIM<sup>+</sup> Cholangiocyte Subcluster via Hypoxia/TGF-β-CREM-VIM Axis.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Scalable Aqueous Polymerization Via Nanoconfinement Effect Generating Two-Dimensional Polymers With Excitation-Dependent Clusteroluminescence.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

A Multi-omics Investigation Identifies TACC3 as a Driver of Immunosuppression in Intrahepatic Cholangiocarcinoma via Activation of the STAT3-PD-L1 Axis.

Journal of clinical and translational hepatology·2026
Same author

PRXL2B facilitates the progression of hepatocellular carcinoma and the therapeutic efficacy of oncolytic adenovirus H101 through the PI3K/AKT/PD-L1 axis.

Bioscience trends·2026
Same journal

Literature Reviews After AI.

Journal of medical imaging (Bellingham, Wash.)·2026
Same journal

Illustration of transfer learning from breast cancer detection to risk prediction: adaptation to local data and local objectives.

Journal of medical imaging (Bellingham, Wash.)·2026
Same journal

RadGazeGen: radiomics and gaze-guided chest X-ray generation using diffusion models.

Journal of medical imaging (Bellingham, Wash.)·2026
Same journal

DDARes-U<sup>2</sup>Net: a dual-decoder adversarial residual U<sup>2</sup>Net algorithm for segmentation of COVID-19 pneumonia lesions.

Journal of medical imaging (Bellingham, Wash.)·2026
Same journal

High-speed optical tracking and augmented reality platform for image-guided interventions.

Journal of medical imaging (Bellingham, Wash.)·2026
Same journal

Transplant-ready? Evaluating AI lung segmentation models in candidates with severe lung disease.

Journal of medical imaging (Bellingham, Wash.)·2026
See all related articles

Related Experiment Video

Updated: Feb 15, 2026

Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System
08:35

Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System

Published on: December 16, 2019

9.8K

Pairwise domain adaptation module for CNN-based 2-D/3-D registration.

Jiannan Zheng1,2, Shun Miao1, Z Jane Wang2

  • 1Siemens Healthineers, Princeton, New Jersey, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|January 30, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a pairwise domain adaptation module to bridge the performance gap in deep learning-based 2-D/3-D registration. The module adapts models trained on synthetic data for improved accuracy with real clinical data.

Keywords:
2-D/3-D registrationconvolutional neural networksimage-guided procedurespairwise domain adaptation module

More Related Videos

Key Factors Affecting the Performance of Sb2S3-sensitized Solar Cells During an Sb2S3 Deposition via SbCl3-thiourea Complex Solution-processing
08:24

Key Factors Affecting the Performance of Sb2S3-sensitized Solar Cells During an Sb2S3 Deposition via SbCl3-thiourea Complex Solution-processing

Published on: July 16, 2018

8.3K
Adapting Gastrointestinal Organoids for Pathogen Infection and Single Cell Sequencing under Biosafety Level 3 BSL-3 Conditions
07:59

Adapting Gastrointestinal Organoids for Pathogen Infection and Single Cell Sequencing under Biosafety Level 3 BSL-3 Conditions

Published on: September 10, 2021

3.6K

Related Experiment Videos

Last Updated: Feb 15, 2026

Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System
08:35

Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System

Published on: December 16, 2019

9.8K
Key Factors Affecting the Performance of Sb2S3-sensitized Solar Cells During an Sb2S3 Deposition via SbCl3-thiourea Complex Solution-processing
08:24

Key Factors Affecting the Performance of Sb2S3-sensitized Solar Cells During an Sb2S3 Deposition via SbCl3-thiourea Complex Solution-processing

Published on: July 16, 2018

8.3K
Adapting Gastrointestinal Organoids for Pathogen Infection and Single Cell Sequencing under Biosafety Level 3 BSL-3 Conditions
07:59

Adapting Gastrointestinal Organoids for Pathogen Infection and Single Cell Sequencing under Biosafety Level 3 BSL-3 Conditions

Published on: September 10, 2021

3.6K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Accurate 2-D/3-D registration is crucial for image-guided therapy, enabling precise alignment of preoperative 3-D data with intraoperative 2-D X-ray images.
  • Deep learning, particularly Convolutional Neural Networks (CNNs), has advanced 2-D/3-D registration accuracy and efficiency.
  • Training deep learning models often requires large annotated clinical datasets, which are difficult to obtain, leading to reliance on synthetic data and a subsequent performance gap on real data.

Purpose of the Study:

  • To develop a flexible and generalizable method for adapting deep learning models trained on synthetic data to perform accurately on clinical 2-D/3-D medical image registration.
  • To address the challenge of limited annotated clinical data by proposing a pairwise domain adaptation (PDA) module.

Main Methods:

  • Proposed a pairwise domain adaptation (PDA) module designed to learn domain-invariant features.
  • The PDA module can be integrated into existing deep learning frameworks and applied to pre-trained CNN models.
  • Employed a strategy requiring only a small amount of paired real and synthetic data for adaptation.

Main Results:

  • Demonstrated significant improvements in generalizability and flexibility for 2-D/3-D medical image registration.
  • Quantitative evaluations on two clinical applications using different deep network frameworks confirmed the module's effectiveness.
  • Successfully adapted models trained on synthetic data to achieve better performance on clinical data.

Conclusions:

  • The proposed pairwise domain adaptation module effectively bridges the gap between synthetic and clinical data for deep learning-based 2-D/3-D registration.
  • The module offers a flexible and generalizable solution applicable to various deep learning frameworks and medical imaging scenarios.
  • This approach facilitates the use of deep learning in image-guided therapy where clinical data is scarce.