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Metabolic tumour volume segmentation for oesophageal cancer on hybrid PET/CT using convolutional network architecture

Spezi, Emiliano, Parkinson, Craig, Berenato, Salvatore, Riviera, Walter, Sobhee, Shaileen, Stylianou, Costas, Crosby, Tom and Foley, Kieran 2020. Metabolic tumour volume segmentation for oesophageal cancer on hybrid PET/CT using convolutional network architecture. Presented at: 33rd Annual European Association of Nuclear Medicine Congress (EANM 2020), Virtual, 22-30 October 2020. European Journal of Nuclear Medicine and Molecular Imaging. , vol. S1. Springer Verlag (Germany), pp. 5481-5482. 10.1007/s00259-020-04988-4
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Abstract

Oesophageal cancer (OC) has a particularly poor prognosis with an overall 5-year survival rate of only 15%. OC is rising in incidence and is a cancer with unmet clinical need. The segmentation of metabolic tumour volume (MTV) is time consuming and subject to intra and inter-observer variability. This study aims to increase the efficiency of MTV segmentation in OC by developing a hybrid PET/CT deep-learned model based on convolutional network architecture.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Springer Verlag (Germany)
ISSN: 1619-7070
Date of First Compliant Deposit: 27 January 2021
Date of Acceptance: 26 June 2020
Last Modified: 27 Jan 2021 14:22
URI: http://orca.cf.ac.uk/id/eprint/137942

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