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

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

doi:10.1007/978-3-031-27420-6_1

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

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. PriorSu Ruan, Stephanie Tanadini-Lang, Olena Tankyevych, Yazdan Salimi, Martin Vallières, Pierre Vera, Dimitris Visvikis, Kareem Wahid, Habib Zaidi, Mathieu Hatt, Adrien Depeursinge

Radiation Oncology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Scopus citations

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.

Original language	English (US)
Title of host publication	Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings
Editors	Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge, Mathieu Hatt
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1-30
Number of pages	30
ISBN (Print)	9783031274190
DOIs	https://doi.org/10.1007/978-3-031-27420-6_1
State	Published - 2023
Event	3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: Sep 22 2022 → Sep 22 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13626 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	9/22/22 → 9/22/22

Keywords

Challenge
Deep learning
Head and neck cancer
Machine learning
Medical imaging
Radiomics
Segmentation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-27420-6_1

Cite this

Andrearczyk, V., Oreiller, V., Abobakr, M., Akhavanallaf, A., Balermpas, P., Boughdad, S., Capriotti, L., Castelli, J., Cheze Le Rest, C., Decazes, P., Correia, R., El-Habashy, D., Elhalawani, H., Fuller, C. D., Jreige, M., Khamis, Y., La Greca, A., Mohamed, A., Naser, M., ... Depeursinge, A. (2023). Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. In V. Andrearczyk, V. Oreiller, A. Depeursinge, & M. Hatt (Eds.), Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings (pp. 1-30). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13626 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-27420-6_1

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. / Andrearczyk, Vincent; Oreiller, Valentin; Abobakr, Moamen et al.
Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings. ed. / Vincent Andrearczyk; Valentin Oreiller; Adrien Depeursinge; Mathieu Hatt. Springer Science and Business Media Deutschland GmbH, 2023. p. 1-30 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13626 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Andrearczyk, V, Oreiller, V, Abobakr, M, Akhavanallaf, A, Balermpas, P, Boughdad, S, Capriotti, L, Castelli, J, Cheze Le Rest, C, Decazes, P, Correia, R, El-Habashy, D, Elhalawani, H, Fuller, CD, Jreige, M, Khamis, Y, La Greca, A, Mohamed, A , Naser, M, Prior, JO, Ruan, S, Tanadini-Lang, S, Tankyevych, O, Salimi, Y, Vallières, M, Vera, P, Visvikis, D, Wahid, K, Zaidi, H, Hatt, M & Depeursinge, A 2023, Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. in V Andrearczyk, V Oreiller, A Depeursinge & M Hatt (eds), Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13626 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 1-30, 3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Singapore, Singapore, 9/22/22. https://doi.org/10.1007/978-3-031-27420-6_1

Andrearczyk V, Oreiller V, Abobakr M, Akhavanallaf A, Balermpas P, Boughdad S et al. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. In Andrearczyk V, Oreiller V, Depeursinge A, Hatt M, editors, Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 1-30. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-27420-6_1

Andrearczyk, Vincent ; Oreiller, Valentin ; Abobakr, Moamen et al. / Overview of the HECKTOR Challenge at MICCAI 2022 : Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings. editor / Vincent Andrearczyk ; Valentin Oreiller ; Adrien Depeursinge ; Mathieu Hatt. Springer Science and Business Media Deutschland GmbH, 2023. pp. 1-30 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f0c22c2bfcc347e98eee50df4244f89a,

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

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 (DSCagg ) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.",

keywords = "Challenge, Deep learning, Head and neck cancer, Machine learning, Medical imaging, Radiomics, Segmentation",

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

note = "Funding Information: valuable work. This challenge and the winner prizes were sponsored by Aquilab France, Bioemtech Greece and Siemens Healthineers Switzerland (500e each, for Task 1, Task 2, and Best Paper). The software used to centralise the annotation and quality control of the GTVp and GTVn regions was MIM (MIM software Inc., Cleveland,OH), which kindly supported the challenge via free licences. This work was also partially supported by the Swiss National Science Foundation (SNSF, grant 205320 179069), the Swiss Personalized Health Network (SPHN, via the IMAGINE and QA4IQI projects) and the RCSO IsNET HECKTOR project. Funding Information: (d) Task 1: The award was 500 euros, sponsored by Aquilab. Task 2: The award was 500 euros, sponsored by Bioemtech. Best paper award: The award was 500 euros, sponsored by Siemens Healthineers Switzerland. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 ; Conference date: 22-09-2022 Through 22-09-2022",

year = "2023",

doi = "10.1007/978-3-031-27420-6_1",

language = "English (US)",

isbn = "9783031274190",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "1--30",

editor = "Vincent Andrearczyk and Valentin Oreiller and Adrien Depeursinge and Mathieu Hatt",

booktitle = "Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings",

address = "Germany",

}

TY - GEN

T1 - Overview of the HECKTOR Challenge at MICCAI 2022

T2 - 3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022

AU - Andrearczyk, Vincent

AU - Oreiller, Valentin

AU - Abobakr, Moamen

AU - Akhavanallaf, Azadeh

AU - Balermpas, Panagiotis

AU - Boughdad, Sarah

AU - Capriotti, Leo

AU - Castelli, Joel

AU - Cheze Le Rest, Catherine

AU - Decazes, Pierre

AU - Correia, Ricardo

AU - El-Habashy, Dina

AU - Elhalawani, Hesham

AU - Fuller, Clifton D.

AU - Jreige, Mario

AU - Khamis, Yomna

AU - La Greca, Agustina

AU - Mohamed, Abdallah

AU - Naser, Mohamed

AU - Prior, John O.

AU - Ruan, Su

AU - Tanadini-Lang, Stephanie

AU - Tankyevych, Olena

AU - Salimi, Yazdan

AU - Vallières, Martin

AU - Vera, Pierre

AU - Visvikis, Dimitris

AU - Wahid, Kareem

AU - Zaidi, Habib

AU - Hatt, Mathieu

AU - Depeursinge, Adrien

N1 - Funding Information: valuable work. This challenge and the winner prizes were sponsored by Aquilab France, Bioemtech Greece and Siemens Healthineers Switzerland (500e each, for Task 1, Task 2, and Best Paper). The software used to centralise the annotation and quality control of the GTVp and GTVn regions was MIM (MIM software Inc., Cleveland,OH), which kindly supported the challenge via free licences. This work was also partially supported by the Swiss National Science Foundation (SNSF, grant 205320 179069), the Swiss Personalized Health Network (SPHN, via the IMAGINE and QA4IQI projects) and the RCSO IsNET HECKTOR project. Funding Information: (d) Task 1: The award was 500 euros, sponsored by Aquilab. Task 2: The award was 500 euros, sponsored by Bioemtech. Best paper award: The award was 500 euros, sponsored by Siemens Healthineers Switzerland. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023

Y1 - 2023

N2 - 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 (DSCagg ) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.

AB - 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 (DSCagg ) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.

KW - Challenge

KW - Deep learning

KW - Head and neck cancer

KW - Machine learning

KW - Medical imaging

KW - Radiomics

KW - Segmentation

UR - http://www.scopus.com/inward/record.url?scp=85151046928&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85151046928&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-27420-6_1

DO - 10.1007/978-3-031-27420-6_1

M3 - Conference contribution

C2 - 37195050

AN - SCOPUS:85151046928

SN - 9783031274190

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

EP - 30

BT - Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings

A2 - Andrearczyk, Vincent

A2 - Oreiller, Valentin

A2 - Depeursinge, Adrien

A2 - Hatt, Mathieu

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 22 September 2022 through 22 September 2022

ER -