Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks

J Med Imaging (Bellingham)

25 October Oct 2022 one month ago
  • Mancini EM, Agalbato C, Pontone G.

Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion).

Reference: Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks.
Killekar A, Grodecki K, Lin A, Cadet S, McElhinney P, Razipour A, Chan C, Pressman BD, Julien P, Chen P, Simon J, Maurovich-Horvat P, Gaibazzi N, Thakur U, Mancini E, Agalbato C, Munechika J, Matsumoto H, Menè R, Parati G, Cernigliaro F, Nerlekar N, Torlasco C, Pontone G, Dey D, Slomka P. J Med Imaging (Bellingham). 2022 Sep;9(5):054001.

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