Abstract
Head and Neck (HN) cancer has the sixth highest incidence rate of all malignancies worldwide. One of the two main curative treatments for this malignancy is radiotherapy, whose delivery depends on accurate contouring of the primary tumor and affected lymph nodes among other structures. In this study, we present a transfer learning-based approach for the automatic primary tumor and lymph nodes segmentation in fused positron emission tomography (PET) and computed tomography (CT) images belonging to the HECKTOR challenge dataset. Transfer learning is performed from the Genesis Chest CT model, a publicly available 3D U-net, pre-trained on chest CT scans. Three-fold cross-validation is employed during training, so that, on each fold, two different binary segmentation models are chosen, one for the primary tumor and one for the lymph nodes. During testing, majority voting is applied. Our results show promising performance on the training and validation cohorts, while moderate performance was observed in the test cohort.
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La Greca Saint-Esteven, A., Motisi, L., Balermpas, P., Tanadini-Lang, S. (2023). A Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal Images of Oropharyngeal Cancer. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022. Lecture Notes in Computer Science, vol 13626. Springer, Cham. https://doi.org/10.1007/978-3-031-27420-6_9
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DOI: https://doi.org/10.1007/978-3-031-27420-6_9
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