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HECKTOR@MICCAI 2022: Singapore
- Vincent Andrearczyk
, Valentin Oreiller
Head and Neck Tumor Segmentation and Outcome Prediction - Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Lecture Notes in Computer Science 13626, Springer 2023, ISBN 978-3-031-27419-0 - Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. 1-30 - Andriy Myronenko
Automated Head and Neck Tumor Segmentation from 3D PET/CT HECKTOR 2022 Challenge Report. 31-37 - Xiao Sun
A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET Images. 38-46 - Hao Jiang, Jason Haimerl, Xuejun Gu, Weiguo Lu:
A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images. 47-53 - Anthony Wang, Ti Bai, Dan Nguyen, Steve B. Jiang:
Octree Boundary Transfiner: Efficient Transformers for Tumor Segmentation Refinement. 54-60 - Arnav Jain
Head and Neck Primary Tumor and Lymph Node Auto-segmentation for PET/CT Scans. 61-69 - Seyed Masoud Rezaeijo, Ali Harimi, Mohammad R. Salmanpour:
Fusion-Based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques. 70-76 - Yaying Shi, Xiaodong Zhang, Yonghong Yan:
Stacking Feature Maps of Multi-scaled Medical Images in U-Net for 3D Head and Neck Tumor Segmentation. 77-85 - Agustina La Greca Saint-Esteven
A Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal Images of Oropharyngeal Cancer. 86-93 - Shadab Ahamed
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 Images. 94-106 - Abhishek Srivastava
Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation. 107-113 - Hung Chu
Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach. 114-120 - Louis Rebaud, Thibault Escobar, Fahad Khalid, Kibrom Girum, Irène Buvat:
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/CT. 121-134 - Mingyuan Meng
Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer. 135-143 - Kai Wang
Recurrence-Free Survival Prediction Under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers. 144-153 - Hui Xu, Yihao Li, Wei Zhao, Gwenolé Quellec
Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT Images. 154-165 - Vajira Thambawita, Andrea M. Storås, Steven Alexander Hicks, Pål Halvorsen, Michael A. Riegler:
MLC at HECKTOR 2022: The Effect and Importance of Training Data When Analyzing Cases of Head and Neck Tumors Using Machine Learning. 166-177 - Ángel Víctor Juanco-Müller
Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients. 178-191 - Qing Lyu
Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT Images. 192-201 - Yiling Wang, Elia Lombardo, Lili Huang, Claus Belka, Marco Riboldi, Christopher Kurz, Guillaume Landry:
Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT Images. 202-211 - Zohaib Salahuddin, Yi Chen, Xian Zhong, Nastaran Mohammadian Rad
HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT Images. 212-220 - Jianan Chen, Anne L. Martel:
Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network. 221-229 - Mohammad R. Salmanpour, Ghasem Hajianfar, Mahdi Hosseinzadeh, Seyed Masoud Rezaeijo, Mohammad Mehdi Hosseini, Ehsanhosein Kalatehjari, Ali Harimi, Arman Rahmim:
Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer. 230-239 - Baoqiang Ma
Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients. 240-254
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