Head and Neck Tumor Segmentation and Outcome Prediction [electronic resource] : Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic -- Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images -- CCUT-Net: Pixel-wise Global Context Channel Attention UT-Net for head and neck tumor segmentation -- A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images -- Automatic Segmentation of Head and Neck (H&N) Primary Tumors in PET and CT images using 3D-Inception-ResNet Model -- The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network -- Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images -- PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT -- The Head and Neck Tumor Segmentation based on 3D U-Net: 3D U-net applied to Simple Attention Module for Head and Neck tumor segmentation in PET and CT images -- Skip-SCSE Multi-Scale Attention and Co-Learning method for Oropharyngeal Tumor Segmentation on multi-modal PET-CT images -- Head and Neck Cancer Primary Tumor Auto Segmentation using Model Ensembling of Deep Learning in PET/CT Images -- Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation -- Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model -- Deep learning based GTV delineation and progression free survival risk score prediction for head and neck cancer patients -- Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer -- PET/CT Head and Neck tumor segmentation and Progression Free Survival prediction using Deep and Machine learning techniques -- Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT images -- Multimodal PET/CT Tumour Segmentation and Progression-Free Survival Prediction using a Full-scale UNet with Attention -- Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer -- Fusion-Based head and neck Tumor Segmentation and Survival prediction using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems -- Head and Neck Primary Tumor Segmentation using Deep Neural Networks and Adaptive Ensembling -- Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks -- Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk Prediction -- Deep Supervoxel Segmentation Survival Anaylsis in Head and Neck Cancer Patients -- A Hybrid Radiomics Approach to Modeling Progression-free Survival in Head and Neck Cancers -- An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data -- Progression Free Survival Prediction for Head and Neck Cancer using Deep Learning based on Clinical and PET/CT Imaging Data -- Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma -- Self-supervised multi-modality image feature extraction for the progression free survival prediction in head and neck cancer -- Comparing deep learning and conventional machine learning for outcome prediction of head and neck cancer in PET/CT.

This book constitutes the Second 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic. The 29 contributions presented, as well as an overview paper, were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 325 delineated PET/CT images was made available for training. .