Abstract
Head and neck cancer is one of the most common cancers in the world. The automatic segmentation of head and neck tumors with the help of computer is of great significance for treatment. In the context of the MICCAI 2021 HEad and neCK tumOR (HECKTOR) segmentation challenge, we propose a combination of a priori and a posteriori attention to segment tumor regions from PET/CT images. Specifically, 1) According to the imaging characteristics of PET, we use the normalized PET as an attention map to emphasize the tumor area on CT as a priori attention. 2) We add channel attention to the model as a posteriori attention. 3) For the test set contains unseen domains, we use Mixup to mix the PET and CT in the train set to simulate unseen domains and enhance the generalization of the network. Our results on the test set are produced with the use of an ensemble of multiple models, and our method ranked third place in the MICCAI 2021 HECKTOR challenge with DSC is 0.7735 and HD95 is 3.0882.
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Lu, J., Lei, W., Gu, R., Wang, G. (2022). Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021. Lecture Notes in Computer Science, vol 13209. Springer, Cham. https://doi.org/10.1007/978-3-030-98253-9_12
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DOI: https://doi.org/10.1007/978-3-030-98253-9_12
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