A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images.

ABSTRACT: The COVID-19 pandemic has extremely threatened human health, and automated algorithms are needed to segment infected regions in the lung using computed tomography (CT). Although several deep convolutional neural networks (DCNNs) have proposed for this purpose, their performance on this task is suppressed due to the limited local receptive field and deficient global reasoning ability. To address these issues, we propose a segmentation network with a novel pixel-wise sparse graph reasoning (PSGR) module for the segmentation of COVID-19 infected regions in CT images. The PSGR module, which is inserted between the encoder and decoder of the network, can improve the modeling of global contextual information. In the PSGR module, a graph is first constructed by projecting each pixel on a node bas

SUBMITTER: Jia H 

PROVIDER: S-EPMC9942482 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature