Table of Contents

• Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CTVincent Andrearczyk, Valentin Oreiller, Moamen Abobakr, Azadeh Akhavanallaf, Panagiotis Balermpas, Sarah Boughdad, Leo Capriotti, Joël Castelli, Catherine Cheze-Le Rest, Pierre Decazes, Ricardo Correia, Dina El-Habashy, Hesham Elhalawani, Clifton D. Fuller, Mario Jreige, Yomna Khamis, Agustina La Greca Saint-Esteven, Abdallah Mohamed, Mohamed Naser, John O. Prior, Su Ruan, Stephanie Tanadini-Lang, Olena Tankyevych, Yazdan Salimi, Martin Vallières, Pierre Vera, Dimitris Visvikis, Kareem Wahid, Habib Zaidi, Mathieu Hatt, Adrien Depeursinge. 1-30 [doi]
• Automated Head and Neck Tumor Segmentation from 3D PET/CT HECKTOR 2022 Challenge ReportAndriy Myronenko, Md Mahfuzur Rahman Siddiquee, Dong Yang 0005, Yufan He, Daguang Xu. 31-37 [doi]
• A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET ImagesXiao Sun, Chengyang An, Lisheng Wang. 38-46 [doi]
• A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT ImagesHao Jiang, Jason Haimerl, Xuejun Gu, Weiguo Lu. 47-53 [doi]
• Octree Boundary Transfiner: Efficient Transformers for Tumor Segmentation RefinementAnthony Wang, Ti Bai, Dan Nguyen, Steve B. Jiang. 54-60 [doi]
• Head and Neck Primary Tumor and Lymph Node Auto-segmentation for PET/CT ScansArnav Jain, Julia Huang, Yashwanth Ravipati, Gregory Cain, Aidan Boyd, Zezhong Ye, Benjamin H. Kann. 61-69 [doi]
• Fusion-Based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning TechniquesSeyed Masoud Rezaeijo, Ali Harimi, Mohammad R. Salmanpour. 70-76 [doi]
• Stacking Feature Maps of Multi-scaled Medical Images in U-Net for 3D Head and Neck Tumor SegmentationYaying Shi, Xiaodong Zhang, Yonghong Yan 0001. 77-85 [doi]
• A Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal Images of Oropharyngeal CancerAgustina La Greca Saint-Esteven, Laura Motisi, Panagiotis Balermpas, Stephanie Tanadini-Lang. 86-93 [doi]
• A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors and Lymph Nodes from Head and Neck Cancer PET/CT ImagesShadab Ahamed, Luke Polson, Arman Rahmim. 94-106 [doi]
• Multi-scale Fusion Methodologies for Head and Neck Tumor SegmentationAbhishek Srivastava, Debesh Jha, Bulent Aydogan, Mohamed E. Abazeed, Ulas Bagci. 107-113 [doi]
• Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning ApproachHung Chu, Luis Ricardo De la O. Arévalo, Wei Tang, Baoqiang Ma, Yan Li, Alessia De Biase, Stefan Both, Johannes A. Langendijk, Peter M. A. van Ooijen, Nanna Maria Sijtsema, Lisanne van Dijk. 114-120 [doi]
• Simplicity Is All You Need: Out-of-the-Box nnUNet Followed by Binary-Weighted Radiomic Model for Segmentation and Outcome Prediction in Head and Neck PET/CTLouis Rebaud, Thibault Escobar, Fahad Khalid, Kibrom Girum, Irène Buvat. 121-134 [doi]
• Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck CancerMingyuan Meng, Lei Bi 0001, Dagan Feng, Jinman Kim. 135-143 [doi]
• Recurrence-Free Survival Prediction Under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck CancersKai Wang 0053, Yunxiang Li, Michael Dohopolski, Tao Peng, Weiguo Lu, You Zhang 0003, Jing Wang 0022. 144-153 [doi]
• Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT ImagesHui Xu, Yihao Li, Wei Zhao, Gwenolé Quellec, Lijun Lu, Mathieu Hatt. 154-165 [doi]
• MLC at HECKTOR 2022: The Effect and Importance of Training Data When Analyzing Cases of Head and Neck Tumors Using Machine LearningVajira Thambawita, Andrea M. Storås, Steven Alexander Hicks, Pål Halvorsen, Michael A. Riegler. 166-177 [doi]
• Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer PatientsÁngel Víctor Juanco-Müller, João F. C. Mota, Keith A. Goatman, Corné Hoogendoorn. 178-191 [doi]
• Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT ImagesQing Lyu. 192-201 [doi]
• Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT ImagesYiling Wang, Elia Lombardo, Lili Huang, Claus Belka, Marco Riboldi, Christopher Kurz, Guillaume Landry. 202-211 [doi]
• HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT ImagesZohaib Salahuddin, Yi Chen, Xian Zhong, Nastaran Mohammadian Rad, Henry C. Woodruff, Philippe Lambin. 212-220 [doi]
• Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural NetworkJianan Chen 0001, Anne L. Martel. 221-229 [doi]
• Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck CancerMohammad R. Salmanpour, Ghasem Hajianfar, Mahdi Hosseinzadeh, Seyed Masoud Rezaeijo, Mohammad Mehdi Hosseini, Ehsanhosein Kalatehjari, Ali Harimi, Arman Rahmim. 230-239 [doi]
• Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer PatientsBaoqiang Ma, Yan Li, Hung Chu, Wei Tang, Luis Ricardo De la O. Arévalo, Jiapan Guo, Peter M. A. van Ooijen, Stefan Both, Johannes A. Langendijk, Lisanne van Dijk, Nanna Maria Sijtsema. 240-254 [doi]