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2023 | OriginalPaper | Chapter

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT

Authors : Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr, Azadeh Akhavanallaf, Panagiotis Balermpas, Sarah Boughdad, Leo Capriotti, Joel Castelli, Catherine Cheze Le Rest, Pierre Decazes, Ricardo Correia, Dina El-Habashy, Hesham Elhalawani, Clifton D. Fuller, Mario Jreige, Yomna Khamis, Agustina La Greca, Abdallah Mohamed, Mohamed Naser, John O. Prior, Su Ruan, Stephanie Tanadini-Lang, Olena Tankyevych, Yazdan Salimi, Martin Vallières, Pierre Vera, Dimitris Visvikis, Kareem Wahid, Habib Zaidi, Mathieu Hatt, Adrien Depeursinge

Published in: Head and Neck Tumor Segmentation and Outcome Prediction

Publisher: Springer Nature Switzerland

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Abstract

This paper presents an overview of the third edition of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge, organized as a satellite event of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. The challenge comprises two tasks related to the automatic analysis of FDG-PET/CT images for patients with Head and Neck cancer (H &N), focusing on the oropharynx region. Task 1 is the fully automatic segmentation of H &N primary Gross Tumor Volume (GTVp) and metastatic lymph nodes (GTVn) from FDG-PET/CT images. Task 2 is the fully automatic prediction of Recurrence-Free Survival (RFS) from the same FDG-PET/CT and clinical data. The data were collected from nine centers for a total of 883 cases consisting of FDG-PET/CT images and clinical information, split into 524 training and 359 test cases. The best methods obtained an aggregated Dice Similarity Coefficient (\(DSC_{agg}\)) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.

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next chapter Automated Head and Neck Tumor Segmentation from 3D PET/CT HECKTOR 2022 Challenge Report
Appendix
Available only for authorised users
Footnotes
2
The target cohort refers to the subjects from whom the data would be acquired in the final biomedical application. It is mentioned for additional information as suggested in BIAS [28], although all data provided for the challenge are part of the challenge cohort.
 
3
The challenge cohort refers to the subjects from whom the challenge data were acquired.
 
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Metadata
Title
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT
Authors
Vincent Andrearczyk
Valentin Oreiller
Moamen Abobakr
Azadeh Akhavanallaf
Panagiotis Balermpas
Sarah Boughdad
Leo Capriotti
Joel Castelli
Catherine Cheze Le Rest
Pierre Decazes
Ricardo Correia
Dina El-Habashy
Hesham Elhalawani
Clifton D. Fuller
Mario Jreige
Yomna Khamis
Agustina La Greca
Abdallah Mohamed
Mohamed Naser
John O. Prior
Su Ruan
Stephanie Tanadini-Lang
Olena Tankyevych
Yazdan Salimi
Martin Vallières
Pierre Vera
Dimitris Visvikis
Kareem Wahid
Habib Zaidi
Mathieu Hatt
Adrien Depeursinge
Copyright Year
2023
Book
Head and Neck Tumor Segmentation and Outcome Prediction

Print ISBN: 978-3-031-27419-0

Electronic ISBN: 978-3-031-27420-6

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-27420-6_1

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