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; Yornna 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

Head Neck Tumor Chall (2022). 2023:13626:1-30. doi: 10.1007/978-3-031-27420-6_1. Epub 2023 Mar 18.

Authors

Vincent Andrearczyk¹, Valentin Oreiller^{1

2}, Moamen Abobakr³, Azadeh Akhavanallaf⁴, Panagiotis Balermpas⁵, Sarah Boughdad², Leo Capriotti⁶, Joel Castelli^{7

8

9}, Catherine Cheze Le Rest^{10

11}, Pierre Decazes⁶, Ricardo Correia², Dina El-Habashy³, Hesham Elhalawani¹², Clifton D Fuller³, Mario Jreige², Yornna Khamis³, Agustina La Greca⁵, Abdallah Mohamed³, Mohamed Naser³, John O Prior², Su Ruan⁶, Stephanie Tanadini-Lang⁵, Olena Tankyevych^{10

11}, Yazdan Salimi⁴, Martin Vallières¹³, Pierre Vera⁶, Dimitris Visvikis¹¹, Kareem Wahid³, Habib Zaidi⁴, Mathieu Hatt¹¹, Adrien Depeursinge^{1

2}

Affiliations

¹ Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
² Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
³ The University of Texas MD Anderson Cancer Center, Houston, USA.
⁴ Geneva University Hospital, Geneva, Switzerland.
⁵ University Hospital Zürich, Zurich, Switzerland.
⁶ Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France.
⁷ Radiotherapy Department, Cancer Institute Eugène Marquis, Rennes, France.
⁸ INSERM, U1099, Rennes, France.
⁹ University of Rennes 1, LTSI, Rennes, France.
¹⁰ Centre Hospitalier Universitaire de Poitiers (CHUP), Poitiers, France.
¹¹ LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
¹² Cleveland Clinic Foundation, Department of Radiation Oncology, Cleveland, OH, USA.
¹³ Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada.

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.

Keywords: Challenge; Deep learning; Head and neck cancer; Machine learning; Medical imaging; Radiomics; Segmentation.

Abstract

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