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ORCID1st HECKTOR@MICCAI 2020: Lima, Peru
- Vincent Andrearczyk
, Valentin Oreiller, Adrien Depeursinge:
Head and Neck Tumor Segmentation - First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings. Lecture Notes in Computer Science 12603, Springer 2021, ISBN 978-3-030-67193-8 - Vincent Andrearczyk, Valentin Oreiller, Mario Jreige, Martin Vallières, Joël Castelli, Hesham Elhalawani, Sarah Boughdad, John O. Prior, Adrien Depeursinge:
Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT. 1-21 - Simeng Zhu, Zhenzhen Dai, Ning Wen:
Two-Stage Approach for Segmenting Gross Tumor Volume in Head and Neck Cancer with CT and PET Imaging. 22-27 - Juanying Xie, Ying Peng:
The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel 'Squeeze & Excitation' Blocks. 28-36 - Andrei Iantsen, Dimitris Visvikis, Mathieu Hatt:
Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images. 37-43 - Yading Yuan:
Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network. 44-52 - Huai Chen, Haibin Chen, Lisheng Wang:
Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT Images. 53-58 - Jun Ma, Xiaoping Yang:
Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET Images. 59-64 - Chinmay Rao, Suraj Pai, Ibrahim Hadzic, Ivan Zhovannik, Dennis Bontempi, Andre Dekker, Jonas Teuwen, Alberto Traverso:
Oropharyngeal Tumour Segmentation Using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge. 65-77 - Kanchan Ghimire, Quan Chen, Xue Feng:
Patch-Based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions. 78-84 - Mohamed A. Naser, Lisanne van Dijk, Renjie He, Kareem A. Wahid, Clifton D. Fuller:
Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images. 85-98 - Fereshteh Yousefirizi, Arman Rahmim:
GAN-Based Bi-Modal Segmentation Using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images. 99-108
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