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Oropharyngeal Tumour Segmentation Using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge

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Head and Neck Tumor Segmentation (HECKTOR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12603))

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Automatic segmentation of tumours and organs at risk can function as a useful support tool in radiotherapy treatment planning as well as for validating radiomics studies on larger cohorts. In this paper, we developed robust automatic segmentation methods for the delineation of gross tumour volumes (GTVs) from planning Computed Tomography (CT) and FDG-Positron Emission Tomography (PET) images of head and neck cancer patients. The data was supplied as part of the MICCAI 2020 HECKTOR challenge. We developed two main volumetric approaches: A) an end-to-end volumetric approach and B) a slice-by-slice prediction approach that integrates 3D context around the slice of interest. We exploited differences in the representations provided by these two approaches by ensembling them, obtaining a Dice score of 66.9% on the held out validation set. On an external and independent test set, a final Dice score of 58.7% was achieved.

C. Rao and S. Pai—Equal contribution.

J. Teuwen and A. Traverso—These authors share senior authorship.

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Correspondence to Chinmay Rao .

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Rao, C. et al. (2021). Oropharyngeal Tumour Segmentation Using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge. In: Andrearczyk, V., Oreiller, V., Depeursinge, A. (eds) Head and Neck Tumor Segmentation. HECKTOR 2020. Lecture Notes in Computer Science(), vol 12603. Springer, Cham.

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  • Print ISBN: 978-3-030-67193-8

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