Can deep learning detect esophageal lesions in PET-CT scans?

Ackerley, Ian, Smith, Rhodri, Scuffham, James, Halling-Brown, Mark, Lewis, Emma, Spezi, Emilio, Prakash, Vineet and Wells, Kevin 2019. Can deep learning detect esophageal lesions in PET-CT scans? Presented at: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Manchester, England, 26 October - 02 November 2019. 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, pp. 1-4. 10.1109/NSS/MIC42101.2019.9059833

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Abstract

PET-CT scans using 18 F-FDG with a co-registered CT scan are increasingly used to detect cancer. This paper compares deep learning-based lesion detection tools trained on PET, CT and combined modality data. 486 pre-contoured scans were used from a retrospective cohort study into esophageal cancer. Scans were partitioned into training, validation and test sets with an 80:10:10 ratio. 1000 image segments were generated from each scan, with tumor present segments located on the contoured lesion and tumor absent segments distributed randomly within the patient but excluding the tumor. PET and CT image segments were used to train a separate dedicated 5-layer convolutional neural networks (CNN). Testing on segments from unseen scans resulted in an accuracy of greater than 95% for the PET data, and greater than 90% for CT data.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Medicine Wales Research Diagnostic Pet Imaging Centre (PETIC)
Publisher:	IEEE
ISBN:	9781728141640
Last Modified:	07 Dec 2022 14:06
URI:	https://orca.cardiff.ac.uk/id/eprint/146517

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