Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT.- Two-stage approach for segmenting gross tumor volume in head and neck cancer with CT and PET imaging.- The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel 'Squeeze & Excitation' Blocks.- Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images.- Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network.- Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images.- Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images.- Oropharyngeal Tumour Segmentation using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge.- Patch-based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions.- Tumor Segmentation in Patients with Head and Neck Cancers using Deep Learning based-on Multi-modality PET/CT Images.- GAN-based Bi-modal Segmentation using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images.