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

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

Edité par CCSD ; Springer Nature Switzerland -

International audience. 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.

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