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 Experiment Video

Updated: Apr 8, 2026

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

3.7K

Deep Learning for Content-Based Medical Image Retrieval in Picture Archiving and Communication Systems for Brain

Chin-Lin Lee1, Tzu-Hsuan Hsu1, Yu-Te Wu2

  • 1Department of Information Management, National Taipei University of Nursing and Health Science, No. 365, Ming-te Rd, Beitou Dist, Taipei City, 112303, Taiwan, +886 2-2822-7101 ext 1230.

JMIR Medical Informatics
|April 6, 2026
PubMed
Summary

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

Automatic Speech Recognition and Large Language Models for Multilingual Pathology Report Generation: Proof-of-Concept Study.

JMIR formative research·2026
Same author

Automated Aortic Quantification Based on Artificial Intelligence: Validation Using Contrast-Enhanced and Non-Contrast CT Scans from the Same Session.

Bioengineering (Basel, Switzerland)·2026
Same author

Assessing the Evolution and Influence of Medical Open Databases on Biomedical Research and Health Care Innovation: A 25-Year Perspective With a Focus on Privacy and Privacy-Enhancing Technologies.

Journal of medical Internet research·2026
Same author

Effects of a Board Game on Tic Management and Psychosocial Functioning in Adolescents With Tourette Syndrome: Randomized Controlled Trial.

JMIR serious games·2025
Same author

Adaptive RAG-Assisted MRI Platform (ARAMP) for Brain Metastasis Detection and Reporting: A Retrospective Evaluation Using Post-Contrast T1-Weighted Imaging.

Bioengineering (Basel, Switzerland)·2025
Same author

Deep Learning in Thoracic Oncology: Meta-Analytical Insights into Lung Nodule Early-Detection Technologies.

Cancers·2025
Same journal

Pregnancy-Related Clinical Codes in Unlikely Populations in Primary Care.

JMIR medical informatics·2026
Same journal

Selecting, Scaling, and Measuring the Value of Ambient AI in a Nonacademic Health System: Multiphase Pilot Study.

JMIR medical informatics·2026
Same journal

Prediction of Early Hospital Admission (≤24 Hours) After Stroke Using Machine Learning and Deep Learning: Multicenter Study From China.

JMIR medical informatics·2026
Same journal

Assessing the Feasibility and Acceptability of Implementing a Preclinic Vital Signs Assessment in Primary Care: Cross-Sectional Pilot Study.

JMIR medical informatics·2026
Same journal

Candidate Passive Sensor Suite Technologies for Tactical Combat Casualty Care Environments: Comparative Assessment Study.

JMIR medical informatics·2026
Same journal

Relevance of the uMap Collaborative Platform as Support for Choropleth Mapping: A Traffic‒Light Statistical Signal Atlas of All-Cause Mortality-First French Lockdown.

JMIR medical informatics·2026
See all related articles
This summary is machine-generated.

This study introduces a content-based medical image retrieval (CBMIR) system for brain MRI, improving clinical workflow efficiency. The system successfully integrates with PACS, enhancing retrieval accuracy for radiologists.

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computer Science

Background:

  • Traditional text-based search is inadequate for large medical imaging archives.
  • Content-Based Medical Image Retrieval (CBMIR) offers visual search but faces integration challenges with Picture Archiving and Communication Systems (PACS).
  • Deep learning advancements in feature extraction have not translated to widespread CBMIR integration in radiology information systems due to protocol barriers.

Purpose of the Study:

  • Develop a CBMIR system for 7 types of brain tumors in brain MRI scans.
  • Enhance clinical workflow and provide quantitative decision support for radiologists through efficient image retrieval.
  • Facilitate evidence-based case comparison and improve retrieval efficiency, rather than directly improving diagnostic accuracy.
Keywords:
CBMIRDICOMDigital Imaging and Communications in MedicinePACSbrain tumorcontent-based medical image retrievaldeep learningpicture archiving and communication systemtumor

Related Experiment Videos

Last Updated: Apr 8, 2026

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

3.7K

Main Methods:

  • Utilized a deep learning-based feature extraction algorithm (GoogLeNet with generalized mean pooling and an embedding layer) for CBMIR.
  • Developed a system tailored for retrieving 7 distinct types of brain tumors from brain MRI.
  • Integrated the CBMIR system into a PACS environment by harmonizing two open-source projects, overcoming protocol barriers.

Main Results:

  • The CBMIR system achieved a mean average precision of 89.16% and a Precision@10 score of 94.08% on a dataset of 15,873 brain MRI images from 658 participants.
  • Demonstrated the performance and robustness of the deep learning-based feature extraction for medical image retrieval.
  • Successfully integrated the CBMIR system into a PACS environment, validating its practical applicability.

Conclusions:

  • Designed and implemented a PACS-integrated CBMIR system for brain MRI.
  • The system enables efficient and accurate retrieval of medical images within a clinical workflow.
  • The successful integration addresses a significant bottleneck in utilizing advanced CBMIR tools in clinical practice.