Abstract is missing.
- Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CTVincent Andrearczyk, Valentin Oreiller, Mario Jreige, Martin Vallières, Joel Castelli, Hesham Elhalawani, Sarah Boughdad, John O. Prior, Adrien Depeursinge. 1-21 [doi]
- Two-Stage Approach for Segmenting Gross Tumor Volume in Head and Neck Cancer with CT and PET ImagingSimeng Zhu, Zhenzhen Dai, Ning Wen. 22-27 [doi]
- The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel 'Squeeze & Excitation' BlocksJuanying Xie, Ying Peng. 28-36 [doi]
- Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT ImagesAndrei Iantsen, Dimitris Visvikis, Mathieu Hatt. 37-43 [doi]
- Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention NetworkYading Yuan. 44-52 [doi]
- Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT ImagesHuai Chen, Haibin Chen, Lisheng Wang. 53-58 [doi]
- Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET ImagesJun Ma, Xiaoping Yang. 59-64 [doi]
- Oropharyngeal Tumour Segmentation Using Ensemble 3D PET-CT Fusion Networks for the HECKTOR ChallengeChinmay Rao, Suraj Pai, Ibrahim Hadzic, Ivan Zhovannik, Dennis Bontempi, Andre Dekker, Jonas Teuwen, Alberto Traverso. 65-77 [doi]
- Patch-Based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated ConvolutionsKanchan Ghimire, Quan Chen, Xue Feng 0001. 78-84 [doi]
- Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT ImagesMohamed A. Naser, Lisanne van Dijk, Renjie He, Kareem A. Wahid, Clifton D. Fuller. 85-98 [doi]
- GAN-Based Bi-Modal Segmentation Using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT ImagesFereshteh Yousefirizi, Arman Rahmim. 99-108 [doi]