Table of Contents

• Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT ImagesVincent Andrearczyk, Valentin Oreiller, Sarah Boughdad, Catherine Cheze-Le Rest, Hesham Elhalawani, Mario Jreige, John O. Prior, Martin Vallières, Dimitris Visvikis, Mathieu Hatt, Adrien Depeursinge. 1-37 [doi]
• CCUT-Net: Pixel-Wise Global Context Channel Attention UT-Net for Head and Neck Tumor SegmentationJiao Wang, Yanjun Peng, Yanfei Guo, Dapeng Li, Jindong Sun. 38-49 [doi]
• A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET ImagesChengyang An, Huai Chen, Lisheng Wang. 50-57 [doi]
• Automatic Segmentation of Head and Neck (H&N) Primary Tumors in PET and CT Images Using 3D-Inception-ResNet ModelAbdul Qayyum, Abdesslam Benzinou, Moona Mazher, Mohamed Abdel-Nasser, Domenec Puig. 58-67 [doi]
• The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention NetworkGuoshuai Wang, Zhengyong Huang, Hao Shen, Zhanli Hu. 68-74 [doi]
• Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT ImagesMinjeong Cho, Yujin Choi, Donghwi Hwang, Si Young Yie, Hanvit Kim, Jae Sung Lee. 75-82 [doi]
• PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CTJintao Ren, Bao-Ngoc Huynh, Aurora Rosvoll Groendahl, Oliver Tomic, Cecilia Marie Futsaether, Stine Sofia Korreman. 83-91 [doi]
• The Head and Neck Tumor Segmentation Based on 3D U-NetJuanying Xie, Ying Peng. 92-98 [doi]
• 3D U-Net Applied to Simple Attention Module for Head and Neck Tumor Segmentation in PET and CT ImagesTao Liu, Yixin Su, Jiabao Zhang, Tianqi Wei, Zhiyong Xiao. 99-108 [doi]
• Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT ImagesAlessia De Biase, Wei Tang, Nikos Sourlos, Baoqiang Ma, Jiapan Guo, Nanna Maria Sijtsema, Peter M. A. van Ooijen. 109-120 [doi]
• Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT ImagesMohamed A. Naser, Kareem A. Wahid, Lisanne van Dijk, Renjie He, Moamen Abobakr Abdelaal, Cem Dede, Abdallah Sherif Radwan Mohamed, Clifton D. Fuller. 121-133 [doi]
• Priori and Posteriori Attention for Generalizing Head and Neck Tumors SegmentationJiangshan Lu, Wenhui Lei, Ran Gu, Guotai Wang. 134-140 [doi]
• Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear ModelKanchan Ghimire, Quan Chen, Xue Feng. 141-149 [doi]
• Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer PatientsDaniel M. Lang, Jan C. Peeken, Stephanie E. Combs, Jan J. Wilkens, Stefan Bartzsch. 150-159 [doi]
• Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck CancerMingyuan Meng, Yige Peng, Lei Bi, Jinman Kim. 160-167 [doi]
• PET/CT Head and Neck Tumor Segmentation and Progression Free Survival Prediction Using Deep and Machine Learning TechniquesAlfonso Martinez-Larraz Solís, Jaime Marti Asenjo, Beatriz Álvarez Rodríguez. 168-178 [doi]
• Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT ImagesYading Yuan, Saba Adabi, Xuefeng Wang. 179-188 [doi]
• Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival Using a Full-Scale UNet with AttentionEmmanuelle Bourigault, Daniel R. McGowan, Abolfazl Mehranian, Bartlomiej W. Papiez. 189-201 [doi]
• Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck CancerMohammad R. Salmanpour, Ghasem Hajianfar, Seyed Masoud Rezaeijo, Mohammad Ghaemi, Arman Rahmim. 202-210 [doi]
• Fusion-Based Head and Neck Tumor Segmentation and Survival Prediction Using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning SystemsMehdi Fatan, Mahdi Hosseinzadeh, Dariush Askari, Hossein Sheikhi, Seyed Masoud Rezaeijo, Mohammad R. Salmanpour. 211-223 [doi]
• Head and Neck Primary Tumor Segmentation Using Deep Neural Networks and Adaptive EnsemblingGowtham Krishnan Murugesan, Eric Brunner, Diana McCrumb, Jithendra Kumar, Jeff VanOss, Stephen Moore 0008, Anderson Peck, Anthony Chang. 224-235 [doi]
• Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural NetworksFereshteh Yousefirizi, Ian Janzen, Natalia Dubljevic, Yueh-En Liu, Chloe Hill, Calum MacAulay, Arman Rahmim. 236-247 [doi]
• Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk PredictionJiyeon Lee, Jimin Kang, Emily Yunha Shin, Regina E. Y. Kim, Minho Lee. 248-256 [doi]
• Deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer PatientsÁngel Víctor Juanco-Müller, João F. C. Mota, Keith A. Goatman, Corné Hoogendoorn. 257-265 [doi]
• A Hybrid Radiomics Approach to Modeling Progression-Free Survival in Head and Neck CancersSebastian Starke, Dominik Thalmeier, Peter Steinbach, Marie Piraud. 266-277 [doi]
• An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal DataNuman Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub. 278-286 [doi]
• Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging DataMohamed A. Naser, Kareem A. Wahid, Abdallah Sherif Radwan Mohamed, Moamen Abobakr Abdelaal, Renjie He, Cem Dede, Lisanne van Dijk, Clifton D. Fuller. 287-299 [doi]
• 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 CarcinomaKareem A. Wahid, Renjie He, Cem Dede, Abdallah Sherif Radwan Mohamed, Moamen Abobakr Abdelaal, Lisanne van Dijk, Clifton D. Fuller, Mohamed A. Naser. 300-307 [doi]
• Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck CancerBaoqiang Ma, Jiapan Guo, Alessia De Biase, Nikos Sourlos, Wei Tang, Peter M. A. van Ooijen, Stefan Both, Nanna Maria Sijtsema. 308-317 [doi]
• Comparing Deep Learning and Conventional Machine Learning for Outcome Prediction of Head and Neck Cancer in PET/CTBao-Ngoc Huynh, Jintao Ren, Aurora Rosvoll Groendahl, Oliver Tomic, Stine Sofia Korreman, Cecilia Marie Futsaether. 318-326 [doi]