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

Rivaroxaban Then Aspirin vs. Aspirin Alone after Total Hip or Knee Arthroplasty.

The New England journal of medicine·2026
Same author

A Novel Swarm Intelligence-Driven Feature Selection for Interpretable Machine Learning in Multiparametric MRI-Based GBM Overall Survival Analysis.

Cancers·2026
Same author

Comorbidity Burden as a Determinant of Treatment Pathway After Percutaneous Cholecystostomy Tube Placement for Acute Cholecystitis: Experience From an Appalachian Tertiary Referral Center.

The American surgeon·2026
Same author

Prostate cancer tissue mapping and stratification using DRAQ5 and Eosin fluorescent labels integrated with AI classification and segmentation algorithms.

PloS one·2026
Same author

An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.

Nature communications·2026
Same author

Pneumonia or something more? Eisenmenger syndrome in a resource-limited setting.

Tropical doctor·2026

Related Experiment Video

Updated: Jul 9, 2025

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

8.5K

RFS+: A Clinically Adaptable and Computationally Efficient Strategy for Enhanced Brain Tumor Segmentation.

Abdulkerim Duman1, Oktay Karakuş2, Xianfang Sun2

  • 1School of Engineering, Cardiff University, Cardiff CF24 3AA, UK.

Cancers
|December 9, 2023
PubMed
Summary

This study introduces a Region-Focused Selection Plus (RFS+) strategy to improve deep learning models for brain tumor segmentation. RFS+ enhances model generalization and quantification, requiring less data and time for training.

Keywords:
U-netbrain tumor segmentationclinical applicationsgeneralizability of deep learning modelmagnetic resonance imaging (MRI)region-focused selection (RFS)

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K
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

48.1K

Related Experiment Videos

Last Updated: Jul 9, 2025

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

8.5K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K
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

48.1K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Automated brain tumor segmentation is crucial for diagnosis and treatment planning using MRI modalities (T1, T1ce, T2, FLAIR).
  • Current deep learning models excel on standardized datasets but face challenges in diverse clinical settings due to variations in image acquisition parameters.
  • A need exists for robust segmentation models that generalize well across different clinical environments.

Purpose of the Study:

  • To introduce and evaluate the novel Region-Focused Selection Plus (RFS+) strategy for improving deep learning-based automatic brain tumor segmentation.
  • To enhance the generalization and quantification capabilities of deep learning models in diverse clinical settings.
  • To reduce the computational resources and training time required for accurate brain tumor segmentation.

Main Methods:

  • Developed the Region-Focused Selection Plus (RFS+) strategy, a targeted approach focusing on individual regions with customized input masks, activation functions, loss functions, and normalization.
  • Employed weighted ensemble learning by identifying top-performing models for specific regions.
  • Investigated multi-class, multi-label, and binary segmentation approaches with various normalization techniques, comparing three U-net variants on the BraTS 2021 validation dataset and a local dataset.

Main Results:

  • The 2D U-net model achieved Dice Similarity Coefficient (DSC) scores of 77.45% (ET), 82.14% (TC), and 90.82% (WT) on the BraTS 2021 validation dataset.
  • The 2D U-net model augmented with RFS+ strategy achieved a superior DSC score of 79.22% for gross tumor volume (GTV) on the local dataset.
  • The RFS+ strategy-enabled model required 10% less training data, 67% less memory, and 92% less training time compared to state-of-the-art models.

Conclusions:

  • The RFS+ strategy effectively enhances the generalizability and quantification of deep learning models for brain tumor segmentation.
  • RFS+ offers a computationally efficient approach, reducing data, memory, and time requirements.
  • This strategy holds significant promise for improving clinical applications of automated brain tumor segmentation.