AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks

Shuo Jin; Bo Wang; Haibo Xu; Chuan Luo; Lai Wei; Wei Zhao; Xuexue Hou; Wenshuo Ma; Zhengqing Xu; Zhuozhao Zheng; Wenbo Sun; Lan Lan; Wei Zhang; Xiangdong Mu; Chenxi Shi; Zhongxiao Wang; Jihae Lee; Zijian Jin; Minggui Lin; Hongbo Jin; Liang Zhang; Jun Guo; Benqi Zhao; Zhizhong Ren; Shuhao Wang; Zheng You; Jiahong Dong; Xinghuan Wang; Jianming Wang; Wei Xu

Similar to FCN-8s, U-Net 14 was a variant of encoder-decoder architecture and employed skip connection as well. The encoder of U-Net employed multi-stage convolutions to capture context features, and the decoder used multi-stage convolutions to fuse the features. Skip connection was applied in every decoder stage to help recover the full spatial resolution of the network output, making U-Net more precise, and thus suitable for biomedical image segmentation. V-Net 15 was a 3D image segmentation approach, where volumetric convolutions were applied instead of processing the input volumes slice-wise. V-Net adopted a volumetric, fully convolutional neural network and could be trained end-to-end. Based on the Dice coefficient between the predicted segmentation and the ground truth annotation, a novel objective function was introduced to cope with the imbalance between the number of foregrounds and background voxels.