Abstract
Head and Neck (H &N) organ-at-risk (OAR) and tumor segmentations are an essential component of radiation therapy planning. The varying anatomic locations and dimensions of H &N nodal Gross Tumor Volumes (GTVn) and H &N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineation methods. The downstream effect of incorrect segmentation can result in unnecessary irradiation of normal organs. Towards a fully automated radiation therapy planning algorithm, we explore the efficacy of multi-scale fusion based deep learning architectures for accurately segmenting H &N tumors from medical scans. Team Name: M &H_lab_NU.
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Acknowledgment
This project is supported by the NIH funding: R01-CA246704 and R01-CA240639. The computations in this paper were performed on equipment provided by the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under contract 270053.
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Srivastava, A., Jha, D., Aydogan, B., Abazeed, M.E., Bagci, U. (2023). Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation. 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_11
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