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

Cross-Sensor and Cross-Population Generalization of Deep Learning Models for Digital Mammography: A Controlled Four-Country Benchmark of Five Backbone Architectures with Statistical Significance Testing.

Sensors (Basel, Switzerland)·2026
Same author

Ultrasensitive Label-Free Electrochemical Detection of <i>Pseudomonas aeruginosa</i> Using a Surface Molecularly Imprinted Polymer-Modified Screen-Printed Electrode.

Polymers·2026
Same author

Enhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images.

Quantitative imaging in medicine and surgery·2026
Same author

Smart Diagnostics: Hierarchical Deep Learning of Acoustic Emission Signals for Early Crack Detection in Zirconia Dental Structures.

Sensors (Basel, Switzerland)·2026
Same author

Bcl-2 upregulates calcium efflux through PMCA and NCX1 to preserve intracellular calcium homeostasis and confer resistance to apoptosis.

Cell calcium·2026
Same author

YAP inactivation-mediated autophagy inhibition contributes to cisplatin resistance in ovarian cancer cells.

Journal of ovarian research·2026

Related Experiment Video

Updated: Nov 10, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.8K

Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep

May Phu Paing1, Supan Tungjitkusolmun1, Toan Huy Bui2

  • 1School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

This study introduces an automated method for segmenting brain infarct lesions using variational mode decomposition and deep learning. This approach aids in early detection and treatment of brain infarction, improving patient outcomes.

Keywords:
U-Netbrain infarctionstrokevariational mode decomposition

More Related Videos

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.2K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.5K

Related Experiment Videos

Last Updated: Nov 10, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.8K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.2K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.5K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Automated segmentation of brain infarct lesions is crucial for timely medical intervention.
  • Existing methods may face challenges in accurately distinguishing infarct tissue from other brain structures.

Purpose of the Study:

  • To develop a fully automated method for segmenting infarct lesions in T1-weighted brain scans.
  • To combine variational mode decomposition and deep learning for enhanced segmentation accuracy.

Main Methods:

  • Variational mode decomposition (VMD) was used for pre-processing to isolate infarct lesions.
  • An overlapped patches strategy was implemented to optimize the deep learning segmentation task.
  • A 3D U-Net model was developed for patch-wise segmentation of infarct lesions.

Main Results:

  • The proposed method achieved promising performance on a dataset of 239 brain scans.
  • Key performance metrics included an average Dice Similarity Coefficient (DSC) of 0.6684.
  • Intersection over Union (IoU) was 0.5022, and Average Symmetric Surface Distance (ASSD) was 0.3932.

Conclusions:

  • The combined VMD and deep learning approach offers an effective automated solution for infarct lesion segmentation.
  • This method has the potential to improve early detection and treatment planning for brain infarction.
  • Further validation on diverse datasets is recommended to confirm generalizability.