Abstract
Radiomics analysis can help patients suffered from head and neck (H&N) cancer customize tailoring treatments. It requires a large number of segmentation of the H&N tumor area in PET and CT images. However, the cost of manual segmentation is extremely high. In this paper, we propose a coarse-to-fine framework to segment the H&N tumor automatically in FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) images. Specifically, we trained three 3D-UNets with residual blocks to make coarse stage, fine stage and refined stage predictions respectively. Experiments show that such a training framework can improve the segmentation quality step by step. We evaluated our framework with Dice Similarity Coefficient (DSC) and Hausdorff Distance at 95% (HD95) of 0.7733 and 3.0882 respectively in the task 1 of the HEad and neCK TumOR segmentation and outcome prediction in PET/CT images (HECKTOR2021) Challenge and ranked second.
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An, C., Chen, H., Wang, L. (2022). A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images. 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_3
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DOI: https://doi.org/10.1007/978-3-030-98253-9_3
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