Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks.

ABSTRACT: 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 the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (LSTM) networks. Utilizing the expert annotations, model training was performed using 5-fold cross-validation to segment ground-glass opacity and high opacity (including consolidation and pleural effusion). The performance of the method was evaluated on CT data sets from 197 patient

SUBMITTER: Grodecki K 

PROVIDER: S-EPMC8020980 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature