Abstract
Automatic segmentation of head and neck (H&N) tumors plays an important and challenging role in clinical practice and radiomics researchers. In this paper, we developed an automated tumor segmentation method based on combined positron emission tomography/computed tomography (PET/CT) images provided by the MICCAI 2021 Head and Neck Tumor (HECKTOR) Segmentation Challenge. Our model takes 3D U-Net as the backbone architecture, on which residual network is added. In addition, we proposed a multi-channel attention network (MCA-Net), which fuses the information of different receptive fields and gives different weights to each channel to better capture image detail information. In the end, our network scored well on the test set (DSC 0.7681, HD95 3.1549) (id: siat).
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Wang, G., Huang, Z., Shen, H., Hu, Z. (2022). The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network. 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_5
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DOI: https://doi.org/10.1007/978-3-030-98253-9_5
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