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.