• Corpus ID: 220870989

Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients

@article{Tilborghs2020ComparativeSO,
title={Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients},
author={Sofie Tilborghs and Ine Dirks and Lucas Fidon and Siri Willems and Tom Eelbode and J. Bertels and Bart Ilsen and Arne Brys and Adriana Dubbeldam and Nico Buls and Panagiotis Gonidakis and Sebasti'an Amador S'anchez and Annemie Snoeckx and Paul M. Parizel and Johan de Mey and Dirk Vandermeulen and Tom Vercauteren and David Robben and Dirk Smeets and Frederik Maes and Jef Vandemeulebroucke and Paul Suetens},
journal={ArXiv},
year={2020},
volume={abs/2007.15546}
}
• Published 29 July 2020
• Medicine, Computer Science
• ArXiv
Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease. There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans. The main tasks of interest are the automatic segmentation of lung and lung lesions in chest CT scans of confirmed or suspected COVID-19 patients. In this study, we…
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