Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for rapid quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (ConvLSTM) networks.
Grodecki K, Killekar A, Lin A, Cadet S, McElhinney P, Razipour A, Chan C, Pressman BD, Julien 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 PJ. Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks. ArXiv 2021 Mar 31;arXiv:2104.00138v1