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

Direct Growth of Na-Ion Conducting Na<sub>3</sub>O<sub>15</sub>Si<sub>6</sub>Y Solid Glass Electrolyte With Reduced Interfacial Resistance for Safer Room-Temperature Sodium-Sulfur Pouch Cells.

Angewandte Chemie (International ed. in English)·2026
Same author

Nerolidol Mitigates 3-Nitropropionic Acid-Induced Neurotoxicity: <i>In Silico</i>, <i>In Vitro</i>, and <i>In Vivo</i> Approach.

Rejuvenation research·2026
Same author

Marine Algae and Neuroprotection: Unlocking the Therapeutic Potential Against Neurodegenerative Diseases.

Phytotherapy research : PTR·2026
Same author

Identifying Flare Prone Spondyloarthritis: Insights From a Prospective Cohort.

Cureus·2026
Same author

Eucalyptol Ameliorates Neuroendocrine Stress-Aggravated Rheumatoid Arthritis Through Anti-inflammatory, Antioxidant, and Chondroprotective Mechanisms Associated with NF-κB and MAPK Signaling Pathways.

Cureus·2026
Same author

Role of Ni and Co Phosphate-MWCNT Interactions in Enhancing Morphine Electrosensing Performance.

Langmuir : the ACS journal of surfaces and colloids·2026

Related Experiment Video

Updated: Sep 17, 2025

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

3.0K

Automatic Multiclass Tissue Segmentation Using Deep Learning in Brain MR Images of Tumor Patients.

Ankit Kandpal1, Puneet Kumar1, Rakesh Kumar Gupta2

  • 1Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi.

Journal of Computer Assisted Tomography
|June 27, 2025
PubMed
Summary

A new pipeline using convolutional neural networks accurately segments brain tissues and tumor lesions in MR images, improving analysis for neurological and oncological studies.

Keywords:
brain tumor segmentationconvolutional neural networksdeep learninggliomamagnetic resonance imaging

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

9.0K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.9K

Related Experiment Videos

Last Updated: Sep 17, 2025

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

3.0K
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

9.0K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Accurate segmentation of brain tissues and lesions in MRI is vital for diagnosing neurological disorders and brain tumors.
  • Current segmentation methods struggle with lesion-inclusive patient data, especially in brain tumor cases.

Purpose of the Study:

  • To develop and validate a robust pipeline for automatic segmentation of brain tissues and tumor lesions using convolutional neural networks (CNNs).
  • To enhance the precision and efficiency of brain MR image analysis for clinical and research applications.

Main Methods:

  • A pipeline was created using the BraTS'21 dataset (1251 patients) and validated on local hospital data (100 patients).
  • Two deep residual U-Net based CNNs were trained for segmenting brain tissues and tumor lesions.
  • Performance was assessed using Dice Similarity Coefficient (DSC) and Volume Similarity (VS) on independent test sets.

Main Results:

  • The pipeline achieved a mean DSC of 0.84 and VS of 0.93 on BraTS'21 test data.
  • On local hospital data, it achieved a mean DSC of 0.78 and VS of 0.91.
  • The pipeline demonstrated superior performance compared to SPM12 in challenging cases.

Conclusions:

  • The developed pipeline provides a reliable, automated solution for segmenting brain tissues and tumor lesions in MR images.
  • Its adaptability supports research and clinical workflows, improving analytical precision in neurology and oncology.
  • This tool has the potential to streamline diagnostic processes and treatment monitoring.