  Development of robust and accurate fully automated methods for medical image
segmentation is crucial in clinical practice and radiomics studies. In this
work, we contributed an automated approach for Head and Neck (H&N) primary
tumor segmentation in combined positron emission tomography / computed
tomography (PET/CT) images in the context of the MICCAI 2020 Head and Neck
Tumor segmentation challenge (HECKTOR). Our model was designed on the U-Net
architecture with residual layers and supplemented with Squeeze-and-Excitation
Normalization. The described method achieved competitive results in
cross-validation (DSC 0.745, precision 0.760, recall 0.789) performed on
different centers, as well as on the test set (DSC 0.759, precision 0.833,
recall 0.740) that allowed us to win first prize in the HECKTOR challenge among
21 participating teams. The full implementation based on PyTorch and the
trained models are available at https://github.com/iantsen/hecktor
