Miaofei Han

TL;DR: A deep learning (DL) based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung and possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings were discussed.

TL;DR: A DL‐based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID‐19 patients andQuantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction.

TL;DR: An Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images is proposed, which demonstrates the effectiveness and robustness of the proposed method on the identification of CO VID-19 in comparison to state-of-the-art methods.

TL;DR: A multi-resolution 3D convolutional neural network to automatically segment kidneys of ADPKD patients from MR images to dramatically reduce the measurement of kidney volume from 40 minutes to about 1 second, which can greatly accelerate the disease staging of AD PKD patients for large clinical trials, promote the development of related drugs, and reduce the burden of physicians.

TL;DR: The customized V-Net evaluated is very robust against various image artifacts, diseases and slice thicknesses, and has much better performance even on the organs with large shape variations than traditional methods.