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

Unexpected dispersion-stabilized tris(terphenylthiolate) complexes, Ln(SAr <sup><i>i</i>Pr6</sup>)<sub>3</sub>, arising from two-electron reduction by Ln(SAr <sup><i>i</i>Pr6</sup>)<sub>2</sub> [Ar <sup><i>i</i>Pr6</sup> = C<sub>6</sub>H<sub>3</sub>-2,6-(C<sub>6</sub>H<sub>2</sub>-2,6,4- <sup><i>i</i></sup> Pr<sub>3</sub>)<sub>2</sub>].

Chemical science·2026
Same author

Comparative assessment of endogenous lipid transfer protein (LTP) level in genetically modified maize for its relevance in safety assessment.

GM crops & food·2026
Same author

Targeting endothelial ERG to mitigate vascular regression in retinopathies.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

The Divergent Reduction Chemistry of Ln(II) Bis(terphenylthiolate) Complexes, Ln(SAr<sup><i>i</i>Pr6</sup>)<sub>2</sub>, Leads to KLn(μ-SAr<sup><i>i</i>Pr6</sup>)<sub>2</sub>, C─H Bond Activation Products, and Two-Electron Reduction Reactivity.

Journal of the American Chemical Society·2025
Same author

Four-electron oxidation and one-electron reduction of the bis(terphenylthiolate) U(II) complex, U(SAr<sup>iPr6</sup>)<sub>2</sub> [Ar<sup>iPr6</sup> = C<sub>6</sub>H<sub>3</sub>-2,6-(C<sub>6</sub>H<sub>2</sub>-2,4,6-<sup>i</sup>Pr<sub>3</sub>)<sub>2</sub>].

Chemical communications (Cambridge, England)·2025
Same author

Targeting endothelial ERG to mitigate vascular regression and neuronal ischemia in retinopathies.

bioRxiv : the preprint server for biology·2025
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

Head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNet.

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 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: May 12, 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

2.6K

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

Yichen An1, Zhimin Wang1, Eric Ma1

  • 1NeuralRad LLC, Madison, WI, 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
|May 8, 2025
PubMed
Summary
This summary is machine-generated.

A novel deep learning model enhances auto-segmentation of head and neck cancer (HNC) tumors in MRI-guided radiotherapy. This improved accuracy aids clinical workflows in radiation oncology.

Keywords:
AutoencoderDeep learningDice similarity coefficientHead and neck cancerMRI-guided radiotherapyMedical image segmentationTumor segmentationnnUNetv2

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

320
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.3K

Related Experiment Videos

Last Updated: May 12, 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

2.6K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

320
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.3K

Area of Science:

  • Radiotherapy and Oncology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Accurate auto-segmentation of gross tumor volumes (GTVs) in head and neck cancer (HNC) is crucial for effective MRI-guided radiotherapy (RT).
  • Current segmentation methods face challenges in precision, impacting clinical workflows.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for enhanced auto-segmentation of GTVs in HNC using MRI-guided RT images.
  • To improve the accuracy and efficiency of tumor delineation in radiation oncology.

Main Methods:

  • Development of a modified nnUNetv2 deep learning framework incorporating an autoencoder architecture.
  • Inclusion of original training images as an additional input channel and utilization of Mean Squared Error (MSE) loss function.
  • Training on 150 HNC patient datasets and private evaluation on 50 test patients for the HNTS-MRG 2024 challenge.

Main Results:

  • Achieved an aggregated Dice Similarity Coefficient (DSCagg) of 0.8516 for metastatic lymph nodes (GTVn).
  • Obtained a DSCagg of 0.7318 for the primary tumor (GTVp), with an average DSCagg of 0.7917 across both structures.
  • Demonstrated that the enhanced nnUNet architecture effectively learned additional image features, improving segmentation accuracy.

Conclusions:

  • The modified nnUNetv2 framework with an autoencoder and combined loss functions significantly enhances auto-segmentation accuracy for HNC in MRI-guided RT.
  • This deep learning approach contributes to more precise and efficient clinical workflows in radiation oncology.
  • The model shows promise for improving treatment planning and delivery in head and neck cancer radiotherapy.