Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

[1]  Wei Lu,et al.  Deep learning for variational multimodality tumor segmentation in PET/CT , 2020, Neurocomputing.

[2]  Lena Maier-Hein,et al.  BIAS: Transparent reporting of biomedical image analysis challenges , 2019, Medical Image Analysis.

[3]  David Dagan Feng,et al.  Co-Learning Feature Fusion Maps From PET-CT Images of Lung Cancer , 2018, IEEE Transactions on Medical Imaging.

[4]  O. Riesterer,et al.  Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer , 2019, Scientific Reports.

[5]  Wei Lu,et al.  Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network , 2018, Physics in medicine and biology.

[6]  Aaron Carass,et al.  Why rankings of biomedical image analysis competitions should be interpreted with care , 2018, Nature Communications.

[7]  Christian Barillot,et al.  The first MICCAI challenge on PET tumor segmentation , 2018, Medical Image Anal..

[8]  T. Gupta,et al.  Interobserver Variability in the Delineation of Gross Tumour Volume and Specified Organs-at-risk During IMRT for Head and Neck Cancers and the Impact of FDG-PET/CT on Such Variability at the Primary Site. , 2017, Journal of medical imaging and radiation sciences.

[9]  Habib Zaidi,et al.  Classification and evaluation strategies of auto‐segmentation approaches for PET: Report of AAPM task group No. 211 , 2017, Medical physics.

[10]  Issam El-Naqa,et al.  Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer , 2017, Scientific Reports.

[11]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[12]  Ulas Bagci,et al.  A review on segmentation of positron emission tomography images , 2014, Comput. Biol. Medicine.