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This paper presents an overview of the second edition of the HEad and neCKTumOR (HECKTOR) challenge, organized as a satellite event of the 24thInternational Conference on Medical Image Computing and Computer AssistedIntervention (MICCAI) 2021. The challenge is composed of three tasks related tothe automatic analysis of PET/CT images for patients with Head and Neck cancer(H&N), focusing on the oropharynx region. Task 1 is the automatic segmentationof H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is theautomatic prediction of Progression Free Survival (PFS) from the sameFDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVtannotations provided to the participants. The data were collected from sixcenters for a total of 325 images, split into 224 training and 101 testingcases. The interest in the challenge was highlighted by the importantparticipation with 103 registered teams and 448 result submissions. The bestmethods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the firsttask, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3,respectively. In all tasks, simplicity of the approach was found to be key toensure generalization performance. The comparison of the PFS predictionperformance in Tasks 2 and 3 suggests that providing the GTVt contour was notcrucial to achieve best results, which indicates that fully automatic methodscan be used. This potentially obviates the need for GTVt contouring, openingavenues for reproducible and large scale radiomics studies including thousandspotential subjects.

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images