Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network

Automatic segmentation is an essential but challenging step for extracting quantitative imaging bio-markers for characterizing head and neck tumor in tumor detection, diagnosis, prognosis, treatment planning and assessment. The HEad and neCK TumOR Segmentation Challenge 2020 (HECKTOR 2020) provides a common platform for comparing different automatic algorithms for segmentation the primary gross target volume (GTV) in the oropharynx region on FDG-PET and CT images. We participated in the image segmentation challenge by developing a fully automatic segmentation network based on encoder-decoder architecture. In order to better integrate information across different scales, we proposed a dynamic scale attention mechanism that incorporates low-level details with high-level semantics from feature maps at different scales. Our framework was trained using the 201 challenge training cases provided by HECKTOR 2020, and achieved an average Dice Similarity Coefficient (DSC) of 0:7505 with cross validation. By testing on the 53 testing cases, our model achieved an average DSC, precision and recall of 0:7318, 0:7851, and 0:7319 respectively, which ranked our method in the fourth place in the challenge (id: deepX)