id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-159554-50077dgk	Shan, Fei	Lung Infection Quantification of COVID-19 in CT Images with Deep Learning	2020-03-10		.txt	text/plain	3544	186	47	For fast manual delineation of training samples and possible manual intervention of automatic results, a human-in-the-loop (HITL) strategy has been adopted to assist radiologists for infection region segmentation, which dramatically reduced the total segmentation time to 4 minutes after 3 iterations of model updating. By reducing and combining feature map channels, not only the model size and inference time are greatly reduced, but also cross-channel features are effectively fused via convolusion, which makes VB-Net more applicable to deal with large 3D volumetric data than traditional V-Net. Training samples with detailed delineation of each infection region are required for the proposed VB-Net. However, it is a labor-intensive work for radiologists to annotate hundreds of COVID-19 CT scans. To quantitatively evaluate the accuracy of segmentation and measurement, infection regions on 300 CT scans of 300 COVID-19 patients were manually contoured by two radiologists (W.S. and F.S., with 12 and 19 years of experience in chest radiology, respectively) to serve as the reference standard.	./cache/cord-159554-50077dgk.txt	./txt/cord-159554-50077dgk.txt