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

Racial and ethnic disparities in clinical outcomes of HER2-positive metastatic breast cancer treated with antibody-drug conjugates.

Journal of the National Cancer Institute·2026
Same author

Brain tumor segmentation in Sub-Saharan Africa patient population: The BraTS-Africa challenge.

Neuro-oncology advances·2026
Same author

BIO26-046: Validating an LLM for Detecting Enrollment Disparities in Breast Cancer Trials.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

BIO26-046: Validating an LLM for Detecting Enrollment Disparities in Breast Cancer Trials.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) dataset.

Scientific data·2026
Same author

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge.

Nature communications·2025

Related Experiment Video

Updated: Oct 7, 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

Deep Residual Separable Convolutional Neural Network for lung tumor segmentation.

Prasad Dutande1, Ujjwal Baid1, Sanjay Talbar1

  • 1Center of Excellence in Signal and Image Processing, SGGS Institute of Engineering and Technology, Nanded, India.

Computers in Biology and Medicine
|January 9, 2022
PubMed
Summary

This study introduces novel deep learning models, DRS-CNN1 and DRS-CNN2, for precise lung cancer tumor delineation in CT scans. The proposed methods significantly improve segmentation accuracy compared to existing techniques.

Keywords:
Atrous convolutionDeep learningLung cancer

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

672
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.5K

Related Experiment Videos

Last Updated: Oct 7, 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
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

672
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.5K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer remains a leading cause of cancer mortality worldwide.
  • Accurate tumor delineation in Computed Tomography (CT) scans is crucial for lung cancer diagnosis and treatment planning.
  • Deep learning approaches are increasingly demonstrating superior performance in medical image analysis compared to traditional methods.

Purpose of the Study:

  • To develop and evaluate a novel deep learning-based methodology for accurate lung cancer tumor segmentation.
  • To introduce two new deep learning networks, Deep Residual Separable Convolutional Neural Network 1 (DRS-CNN1) and 2 (DRS-CNN2), for improved tumor delineation.
  • To compare the performance of the proposed networks against state-of-the-art segmentation models.

Main Methods:

  • A deep learning methodology incorporating maximum intensity projection pre-processing, two novel DRS-CNN architectures, and an ensemble strategy was developed.
  • The proposed DRS-CNN1 and DRS-CNN2 models were trained and evaluated.
  • Performance comparison was conducted against the U-net network and other segmentation networks on the Medical Segmentation Decathlon (MSD) and StructSeg 2019 datasets.

Main Results:

  • The DRS-CNN networks demonstrated superior performance over the U-net network and other contemporary segmentation models.
  • DRS-CNN2 achieved a mean Dice Similarity Coefficient (DSC) of 0.649, mean 95 Hausdorff Distance (HD95) of 18.26, mean Sensitivity of 0.737, and mean Precision of 0.765 on independent test sets.
  • The proposed ensemble strategy further enhanced segmentation accuracy.

Conclusions:

  • The developed deep learning methodology, particularly DRS-CNN2, offers a promising approach for accurate lung cancer tumor segmentation.
  • These findings suggest that the novel DRS-CNN architectures can significantly advance the capabilities of automated tumor delineation in medical imaging.
  • The proposed methods hold potential for improving clinical decision-making in lung cancer diagnosis and treatment.