Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images
This work addresses a domain-specific problem for medical imaging and radiomics, with incremental improvements in segmentation accuracy.
The authors tackled automated segmentation of head and neck tumors in PET/CT images, achieving competitive results with a Dice score of 0.759 on a test set and winning first prize in a challenge among 21 teams.
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