Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN Article Swipe

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Marc Connell , Yi Xin , Sarah E. Gerard , Jacob Herrmann , Parth Shah , Kevin Martin , Emanuele Rezoagli , Davide Ippolito , Jennia Rajaei , Ryan Baron , P. Delvecchio , S. Humayun , Rahim R. Rizi , Giacomo Bellani , Maurizio Cereda ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1016/j.ymeth.2022.07.007

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Artificial Intelligence

Infectious Diseases (Medical Specialty)

Disease

Concepts

Voxel Hounsfield scale Segmentation Sørensen–Dice coefficient Medicine Coronavirus disease 2019 (COVID-19) Nuclear medicine Lesion Artificial intelligence Radiology Computer science Pattern recognition (psychology) Computed tomography Image segmentation Pathology Infectious disease (medical specialty) Disease

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.ymeth.2022.07.007
OA Status: green
Cited By: 11
References: 31
Related Works: 10
OpenAlex ID: https://openalex.org/W4284960718

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OpenAlex ID: https://openalex.org/W4284960718

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DOI: https://doi.org/10.1016/j.ymeth.2022.07.007

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Title: Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-07-08

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Authors: Marc Connell, Yi Xin, Sarah E. Gerard, Jacob Herrmann, Parth Shah, Kevin Martin, Emanuele Rezoagli, Davide Ippolito, Jennia Rajaei, Ryan Baron, P. Delvecchio, S. Humayun, Rahim R. Rizi, Giacomo Bellani, Maurizio Cereda

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Landing page: https://doi.org/10.1016/j.ymeth.2022.07.007

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Open access: Yes

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OA status: green

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OA URL: https://www.ncbi.nlm.nih.gov/pmc/articles/9288584

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Concepts: Voxel, Hounsfield scale, Segmentation, Sørensen–Dice coefficient, Medicine, Coronavirus disease 2019 (COVID-19), Nuclear medicine, Lesion, Artificial intelligence, Radiology, Computer science, Pattern recognition (psychology), Computed tomography, Image segmentation, Pathology, Infectious disease (medical specialty), Disease

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Cited by: 11

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Citations by year (recent): 2025: 3, 2024: 6, 2023: 2

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References (count): 31

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Related works (count): 10

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