Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Studies I: CT and MRI

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Towards anatomical robust plan evaluation using a population-based model for head and neck cancer proton therapy.

Physics in medicine and biology·2026

Same author

Improving confidence in MRI-based auto-segmentation via uncertainty assessment.

Acta oncologica (Stockholm, Sweden)·2026

Same author

Understanding and leveraging uncertainties in autosegmentation for radiotherapy.

BJR artificial intelligence·2026

Same author

Quality in specialist palliative care for patients with pre-existing severe mental disorders: A retrospective cohort study.

Palliative medicine·2026

Same author

A tumor control probability model for elective nodal irradiation to balance toxicity and regional tumor control in treatment plan optimization for head-and-neck squamous cell carcinoma.

Physics in medicine and biology·2026

Same author

Modeling bilateral lymphatic head and neck tumor progression for personalized elective target volume definition.

Physics in medicine and biology·2026

Search research articles

Related Experiment Video

Updated: Oct 28, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor

Jintao Ren^1,2,3, Jesper Grau Eriksen^1,4, Jasper Nijkamp^1,2

¹Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.

Acta Oncologica (Stockholm, Sweden)

|July 15, 2021

Summary

Deep learning models can assist in delineating head and neck tumors for radiotherapy. Combining multiple imaging types, especially PET, improves accuracy, with ensemble methods showing the most promise.

Keywords:

CNN Deep learning GTV UNet auto-segmentation head and neck cancer

More Related Videos

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Published on: January 11, 2020

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Related Experiment Videos

Last Updated: Oct 28, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Published on: January 11, 2020

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Area of Science:

Radiotherapy
Medical Imaging
Artificial Intelligence

Background:

Manual delineation of Gross Tumor Volume (GTV) for radiotherapy is time-consuming and prone to inter-observer variability.
CT, PET, and MRI are used to improve delineation accuracy due to their complementary characteristics.

Purpose of the Study:

To investigate deep learning for assisting GTV delineation in head and neck squamous cell carcinoma (HNSCC).
To compare the effectiveness of various imaging modality combinations for deep learning-based GTV segmentation.

Main Methods:

A retrospective study of 153 HNSCC patients with CT, PET, and MRI data.
A residual 3D UNet deep learning model was trained on four modality combinations: CT-PET-MRI, CT-MRI, CT-PET, and PET-MRI.
An ensemble model was created by averaging the results of three bi-modality combinations (CT-PET, CT-MRI, PET-MRI).
Segmentation accuracy was evaluated using Dice similarity coefficient (Dice), Hausdorff Distance 95 percentile (HD95), and Mean Surface Distance (MSD).

Main Results:

All imaging combinations including PET achieved similar average scores (Dice: 0.72-0.74, HD95: 8.8-9.5 mm, MSD: 2.6-2.8 mm).
CT-MRI alone yielded lower scores (Dice: 0.58, HD95: 12.9 mm, MSD: 3.7 mm).
The ensemble of three bi-modality combinations achieved the best performance (Dice: 0.74, HD95: 7.9 mm, MSD: 2.4 mm).

Conclusions:

Multimodal deep learning effectively performs auto-segmentation of HNSCC GTV.
Inclusion of PET imaging is crucial for accurate segmentation.
An ensemble approach combining bi-modality networks significantly improved segmentation accuracy over individual models.