  This paper presents an overview of the second edition of the HEad and neCK
TumOR (HECKTOR) challenge, organized as a satellite event of the 24th
International Conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI) 2021. The challenge is composed of three tasks related to
the 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 segmentation
of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the
automatic prediction of Progression Free Survival (PFS) from the same
FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt
annotations provided to the participants. The data were collected from six
centers for a total of 325 images, split into 224 training and 101 testing
cases. The interest in the challenge was highlighted by the important
participation with 103 registered teams and 448 result submissions. The best
methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first
task, 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 to
ensure generalization performance. The comparison of the PFS prediction
performance in Tasks 2 and 3 suggests that providing the GTVt contour was not
crucial to achieve best results, which indicates that fully automatic methods
can be used. This potentially obviates the need for GTVt contouring, opening
avenues for reproducible and large scale radiomics studies including thousands
potential subjects.
