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

Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813, Parkinson, Craig ORCID: https://orcid.org/0000-0003-3454-4957, 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. European Journal of Nuclear Medicine and Molecular Imaging. , vol.47 (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:	05 Nov 2022 03:55
URI:	https://orca.cardiff.ac.uk/id/eprint/137942

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