Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability

Yang Wang, Junkai Zhu, Jinli Zhao, Wenyi Li, Xin Zhang, Xiaolin Meng, Taige Chen, Ming Li, Meiping Ye, Renfang Hu, Shidan Dou, Huayin Hao (+8 others)
2022 Frontiers in Neurology  
BackgroundComputed tomography (CT) plays an essential role in classifying stroke, quantifying penumbra size and supporting stroke-relevant radiomics studies. However, it is difficult to acquire standard, accurate and repeatable images during follow-up. Therefore, we invented an intelligent CT to evaluate stroke during the entire follow-up.MethodsWe deployed a region proposal network (RPN) and V-Net to endow traditional CT with intelligence. Specifically, facial detection was accomplished by
more » ... tifying adjacent jaw positions through training and testing an RPN on 76,382 human faces using a preinstalled 2-dimensional camera; two regions of interest (ROIs) were segmented by V-Net on another training set with 295 subjects, and the moving distance of scanning couch was calculated based on a pre-generated calibration table. Multiple cohorts including 1,124 patients were used for performance validation under three clinical scenarios.ResultsCranial Automatic Planbox Imaging Towards AmeLiorating neuroscience (CAPITAL)-CT was invented. RPN model had an error distance of 4.46 ± 0.02 pixels with a success rate of 98.7% in the training set and 100% with 2.23 ± 0.10 pixels in the testing set. V-Net-derived segmentation maintained a clinically tolerable distance error, within 3 mm on average, and all lines presented with a tolerable angle error, within 3° on average in all boundaries. Real-time, accurate, and repeatable automatic scanning was accomplished with and a lower radiation exposure dose (all P < 0.001).ConclusionsCAPITAL-CT generated standard and reproducible images that could simplify the work of radiologists, which would be of great help in the follow-up of stroke patients and in multifield research in neuroscience.
doi:10.3389/fneur.2022.755492 pmid:35359626 pmcid:PMC8961979 fatcat:6mtuygke7ngp7arbf7yjahpzrq