Objective: CT provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study is to develop a deep learning (DL) based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. Methods: The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each 8 9

Ultrathin MoS2 nanosheets uniformly embedded into a N,O-codoped carbon matrix possess outstanding cyclability and rate performances as a lithium/sodium ion battery anode.

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