Abstract
Delineation of head and neck lesions are crucial for radiation treatment planning and follow-up studies. In this paper we developed an automated segmentation method for head and neck primary and nodal gross tumor volumes (GTVp and GTVn) segmentation in positron emission tomography/computed tomography (PET/CT) provided by the MICCAI 2022 Head and Neck Tumor Segmentation Challenge (HECKTOR 2022). Our segmentation algorithm takes nnU-Net as the backbone and uses dedicated pre- and post-processing to improve the auto-segmentation performance. The pipeline described achieved DSC results of 0.77212 (GTVp 0.77485 and GTVn 0.76938) in the testing dataset of HECTOR 2022. The developed auto-segmentation method is further extensively developed to a web-based platform to permit easy access and facilitate clinical workflow.
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Jiang, H., Haimerl, J., Gu, X., Lu, W. (2023). A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images. 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_4
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