Two-stage hybrid network for segmentation of COVID-19 pneumonia lesions in CT images: a multicenter study.

ABSTRACT: COVID-19 has been spreading continuously since its outbreak, and the detection of its manifestations in the lung via chest computed tomography (CT) imaging is essential to investigate the diagnosis and prognosis of COVID-19 as an indispensable step. Automatic and accurate segmentation of infected lesions is highly required for fast and accurate diagnosis and further assessment of COVID-19 pneumonia. However, the two-dimensional methods generally neglect the intraslice context, while the three-dimensional methods usually have high GPU memory consumption and calculation cost. To address these limitations, we propose a two-stage hybrid UNet to automatically segment infected regions, which is evaluated on the multicenter data obtained from seven hospitals. Moreover, we train a 3D-ResNet for CO

SUBMITTER: Shang Y 

PROVIDER: S-EPMC9294771 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature