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ORCID2nd HECKTOR@MICCAI 2021: Strasbourg, France
- Vincent Andrearczyk
, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge:
Head and Neck Tumor Segmentation and Outcome Prediction - Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Lecture Notes in Computer Science 13209, Springer 2022, ISBN 978-3-030-98252-2 - Vincent Andrearczyk, Valentin Oreiller, Sarah Boughdad, Catherine Cheze Le Rest, Hesham Elhalawani, Mario Jreige, John O. Prior, Martin Vallières, Dimitris Visvikis, Mathieu Hatt, Adrien Depeursinge:
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images. 1-37 - Jiao Wang, Yanjun Peng, Yanfei Guo, Dapeng Li, Jindong Sun:
CCUT-Net: Pixel-Wise Global Context Channel Attention UT-Net for Head and Neck Tumor Segmentation. 38-49 - Chengyang An, Huai Chen, Lisheng Wang:
A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images. 50-57 - Abdul Qayyum, Abdesslam Benzinou, Moona Mazher, Mohamed Abdel-Nasser, Domenec Puig:
Automatic Segmentation of Head and Neck (H&N) Primary Tumors in PET and CT Images Using 3D-Inception-ResNet Model. 58-67 - Guoshuai Wang, Zhengyong Huang, Hao Shen, Zhanli Hu:
The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network. 68-74 - Minjeong Cho, Yujin Choi, Donghwi Hwang, Si Young Yie, Hanvit Kim, Jae Sung Lee:
Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images. 75-82 - Jintao Ren, Bao-Ngoc Huynh, Aurora Rosvoll Groendahl, Oliver Tomic, Cecilia Marie Futsaether, Stine Sofia Korreman:
PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT. 83-91 - Juanying Xie, Ying Peng:
The Head and Neck Tumor Segmentation Based on 3D U-Net. 92-98 - Tao Liu, Yixin Su, Jiabao Zhang, Tianqi Wei, Zhiyong Xiao:
3D U-Net Applied to Simple Attention Module for Head and Neck Tumor Segmentation in PET and CT Images. 99-108 - Alessia De Biase, Wei Tang, Nikos Sourlos, Baoqiang Ma, Jiapan Guo, Nanna Maria Sijtsema, Peter M. A. van Ooijen:
Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images. 109-120 - Mohamed A. Naser, Kareem A. Wahid, Lisanne van Dijk, Renjie He, Moamen Abobakr Abdelaal, Cem Dede, Abdallah Sherif Radwan Mohamed, Clifton D. Fuller:
Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images. 121-133 - Jiangshan Lu, Wenhui Lei, Ran Gu, Guotai Wang:
Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation. 134-140 - Kanchan Ghimire, Quan Chen, Xue Feng:
Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model. 141-149 - Daniel M. Lang, Jan C. Peeken, Stephanie E. Combs, Jan J. Wilkens, Stefan Bartzsch:
Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients. 150-159 - Mingyuan Meng, Yige Peng, Lei Bi, Jinman Kim:
Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer. 160-167 - Alfonso Martinez-Larraz Solís, Jaime Marti Asenjo, Beatriz Álvarez Rodríguez:
PET/CT Head and Neck Tumor Segmentation and Progression Free Survival Prediction Using Deep and Machine Learning Techniques. 168-178 - Yading Yuan, Saba Adabi, Xuefeng Wang:
Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT Images. 179-188 - Emmanuelle Bourigault, Daniel R. McGowan, Abolfazl Mehranian, Bartlomiej W. Papiez:
Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival Using a Full-Scale UNet with Attention. 189-201 - Mohammad R. Salmanpour, Ghasem Hajianfar, Seyed Masoud Rezaeijo, Mohammad Mahdi Ghaemi, Arman Rahmim:
Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer. 202-210 - Mehdi Fatan, Mahdi Hosseinzadeh, Dariush Askari, Hossein Sheikhi, Seyed Masoud Rezaeijo, Mohammad R. Salmanpour:
Fusion-Based Head and Neck Tumor Segmentation and Survival Prediction Using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems. 211-223 - Gowtham Krishnan Murugesan, Eric Brunner, Diana McCrumb, Jithendra Kumar, Jeff VanOss, Stephen Moore, Anderson Peck, Anthony Chang:
Head and Neck Primary Tumor Segmentation Using Deep Neural Networks and Adaptive Ensembling. 224-235 - Fereshteh Yousefirizi, Ian Janzen, Natalia Dubljevic, Yueh-En Liu, Chloe Hill, Calum MacAulay, Arman Rahmim:
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks. 236-247 - Jiyeon Lee, Jimin Kang, Emily Yunha Shin, Regina E. Y. Kim, Minho Lee:
Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk Prediction. 248-256 - Ángel Víctor Juanco-Müller, João F. C. Mota, Keith A. Goatman, Corné Hoogendoorn:
Deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer Patients. 257-265 - Sebastian Starke, Dominik Thalmeier, Peter Steinbach, Marie Piraud:
A Hybrid Radiomics Approach to Modeling Progression-Free Survival in Head and Neck Cancers. 266-277 - Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub:
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data. 278-286 - Mohamed A. Naser, Kareem A. Wahid, Abdallah Sherif Radwan Mohamed, Moamen Abobakr Abdelaal, Renjie He, Cem Dede, Lisanne van Dijk, Clifton D. Fuller:
Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data. 287-299 - Kareem A. Wahid, Renjie He, Cem Dede, Abdallah Sherif Radwan Mohamed, Moamen Abobakr Abdelaal, Lisanne van Dijk, Clifton D. Fuller, Mohamed A. Naser:
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. 300-307 - Baoqiang Ma, Jiapan Guo, Alessia De Biase, Nikos Sourlos, Wei Tang, Peter M. A. van Ooijen, Stefan Both, Nanna Maria Sijtsema:
Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer. 308-317 - Bao-Ngoc Huynh, Jintao Ren, Aurora Rosvoll Groendahl, Oliver Tomic, Stine Sofia Korreman, Cecilia Marie Futsaether:
Comparing Deep Learning and Conventional Machine Learning for Outcome Prediction of Head and Neck Cancer in PET/CT. 318-326
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