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.
CITATION STYLE
Srivastava, A., Jha, D., Aydogan, B., Abazeed, M. E., & Bagci, U. (2023). Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13626 LNCS, pp. 107–113). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-27420-6_11
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