Evidence map›Paper›PMID 40417457›Full record

ArticleHead 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, proceedings2025

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

Kaiyuan Ji, Zhihan Wu, Jing Han, Jun Jia, Guangtao Zhai, Jiannan Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in 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. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 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
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Kaiyuan JiSchool of Communication and Electronic Engineering, East China Normal University, Shanghai, China.ORCID 0009-0006-2331-3298
Zhihan WuDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0009-0003-0349-1650
Jing HanDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jun JiaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Guangtao ZhaiSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.ORCID 0000-0001-8165-9322
Jiannan LiuDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac deviceR01DE028290 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI CHRISTODOULEAS, JOHN PAUL, FULLER, CLIFTON DAVID · 2019 to 2023
$4.3M
SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck CancerR01CA257814 · NCI · RICE UNIVERSITY · PI FULLER, CLIFTON DAVID, SCHAEFER, ANDREW J · 2021 to 2024
$1.8M
NCI NIH HHS R01 CA257814NIDCR NIH HHS R01 DE028290
6 · The paper itself

Abstract

This article explores the potential of deep learning technologies for the automated identification and delineation of primary tumor volumes (GTVp) and metastatic lymph nodes (GTVn) in radiation therapy planning, specifically using MRI data. Utilizing the high-quality dataset provided by the 2024 MICCAI Head and Neck Tumor Segmentation Challenge, this study employs the 3DnnU-Net model for automatic tumor segmentation. Our experiments revealed that the model performs poorly with high background ratios, which prompted a retraining with selected data of specific background ratios to improve segmentation performance. The results demonstrate that the model performs well on data with low background ratios, but optimization is still needed for high background ratios. Additionally, the model shows better performance in segmenting GTVn compared to GTVp, with DSCagg scores of 0.6381 and 0.8064 for Task 1 and Task 2, respectively, during the final test phase. Future work will focus on optimizing the model and adjusting the network architecture, aiming to enhance the segmentation of GTVp while maintaining the effectiveness of GTVn segmentation to increase accuracy and reliability in clinical applications.

Indexed as

Artificial IntelligenceDeep LearningMedical ImageTumor Segmentation

Identifiers

PMID40417457
PMCPMC12097725

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.