Abstract
In many computer vision areas, deep learning-based models achieved state-of-the-art performances and started to catch the attention in the context of medical imaging. The emergence of deep learning is significantly cutting-edge the state-of-the-art in medical image segmentation. For generalization and better optimization of current deep learning models for head and neck segmentation problems, head and neck tumor segmentation and outcome prediction in PET/CT images (HECKTOR21) challenge offered the opportunity to participants to develop automatic bi-modal approaches for the 3D segmentation of Head and Neck (H&N) tumors in PET/CT scans, focusing on oropharyngeal cancers. In this paper, a 3D Inception ResNet-based deep learning model (3D-Inception-ResNet) for head and neck tumor segmentation has been proposed. The 3D-Inception module has been introduced at the encoder side and the 3D ResNet module has been proposed at the decoder side. The 3D squeeze and excitation (SE) module is also inserted in each encoder block of the proposed model. The 3D depth-wise convolutional layer has been used in 3D inception and 3D ResNet module to get the optimized performance. The proposed model produced optimal Dice coefficients (DC) and HD95 scores and could be useful for the segmentation of head and neck tumors in PET/CT images. The code is publicly available (https://github.com/RespectKnowledge/HeadandNeck21_3D_Segmentation).
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Qayyum, A., Benzinou, A., Mazher, M., Abdel-Nasser, M., Puig, D. (2022). Automatic Segmentation of Head and Neck (H&N) Primary Tumors in PET and CT Images Using 3D-Inception-ResNet Model. 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_4
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DOI: https://doi.org/10.1007/978-3-030-98253-9_4
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