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

RADIANT: A fully configurable radiotherapy dose prediction framework.

Biomedical physics & engineering express·2026
Same author

Intratumoral sotigalimab with pembrolizumab induces rapid activation of antigen presenting cells and drives anti-tumor responses in non-injected tumors in metastatic melanoma: A phase I/II study.

Cancer discovery·2026
Same author

Renal Oncocytic Neoplasms: Review of Classification Updates, Imaging, and Management.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same author

Cowden Syndrome: Imaging Review and Cancer Surveillance.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same author

Leveraging large language models for heuristic usability assessment of medical software: Insights with the Radiation Planning Assistant.

Journal of applied clinical medical physics·2026
Same author

Retroperitoneum and Pelvic Extraperitoneum: Anatomic Landmarks, Imaging Features, and Patterns of Disease Spread.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same journal

Ensemble of LinkNet Networks for Head and Neck Tumor Segmentation.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings·2025
Same journal

Application of 3D nnU-Net with Residual Encoder in the 2024 MICCAI Head and Neck Tumor Segmentation Challenge.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings·2025
Same journal

Assessing Self-supervised xLSTM-UNet Architectures for Head and Neck Tumor Segmentation in MR-Guided Applications.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings·2025
Same journal

Enhancing nnUNetv2 Training with Autoencoder Architecture for Improved Medical Image Segmentation.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings·2025
Same journal

Enhancing Head and Neck Tumor Segmentation in MRI: The Impact of Image Preprocessing and Model Ensembling.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings·2025
Same journal

Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings·2025
See all related articles

Related Experiment Video

Updated: Jun 15, 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

8.4K

Head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNet.

Awj Twam1, Adrian Celaya1, Evan Lim2

  • 1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Head and Neck Tumor Segmentation for Mr-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, Proceedings
|June 11, 2025
PubMed
Summary
This summary is machine-generated.

Team Pocket utilized PocketNet, a lightweight convolutional neural network (CNN), for automated head and neck cancer (HNC) tumor segmentation. This AI approach achieved promising results in segmenting primary and nodal tumors in MR images, aiding treatment planning.

Keywords:
Automated SegmentationConvolutional Neural NetworksGross Tumor VolumeHead and Neck Cancer

More Related Videos

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

2.7K
Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain
09:29

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Published on: July 29, 2022

2.6K

Related Experiment Videos

Last Updated: Jun 15, 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

8.4K
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

2.7K
Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain
09:29

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Published on: July 29, 2022

2.6K

Area of Science:

  • Medical imaging and artificial intelligence
  • Oncology and radiation therapy
  • Computational anatomy

Background:

  • Head and neck cancer (HNC) poses a significant global health challenge, necessitating precise tumor volume delineation for effective treatment planning.
  • Manual segmentation of gross tumor volumes (GTV) in MR images is time-consuming, labor-intensive, and subject to inter-observer variability.
  • Automated segmentation techniques, particularly those employing deep learning, are crucial for improving efficiency and consistency in HNC treatment.

Purpose of the Study:

  • To evaluate the efficacy of PocketNet, a lightweight convolutional neural network (CNN), for automated segmentation of primary (GTVp) and nodal (GTVn) gross tumor volumes in head and neck cancer (HNC) from pre-radiotherapy MR images.
  • To assess the performance of PocketNet within the context of the HNTS-MRG 2024 Grand Challenge, Task 1.
  • To demonstrate the potential of automated segmentation for enhancing HNC treatment workflows.

Main Methods:

  • Application of PocketNet, a lightweight CNN architecture, for the segmentation of GTVp and GTVn in MR images.
  • Participation in Task 1 of the HNTS-MRG 2024 Grand Challenge, focusing on pre-radiotherapy MR data.
  • Quantitative evaluation using the Dice Sorensen Coefficient (DSCagg) for performance assessment.

Main Results:

  • PocketNet achieved an aggregated Dice Sorensen Coefficient (DSCagg) of 0.808 for GTVn and 0.732 for GTVp.
  • The overall mean performance across both tumor types was 0.77.
  • These results indicate robust performance in automated tumor segmentation for HNC.

Conclusions:

  • PocketNet demonstrates significant potential as an efficient and accurate tool for automated GTV segmentation in MR-guided HNC interventions.
  • The developed approach shows promise for integration into clinical workflows, potentially improving treatment planning and delivery.
  • Further optimization of the PocketNet architecture and training strategies may lead to enhanced segmentation accuracy for HNC.