%0 Journal Article
%A KANG Bo
%A LIU Yi-lin
%A MENG Xiang-fei
%A ZHANG Tian-qi
%A ZHOU Ying
%T U-Net Based Intracranial Hemorrhage Recognition
%D 2020
%R 10.13190/j.jbupt.2019-146
%J Journal of Beijing University of Posts and Telecommunications
%P 92-98
%V 43
%N 3
%X Aiming at analysis and recognition of cerebral hemorrhage from craniocerebral computed tomography (CT) images, a method combining neural network model U-Net with contour recognition is proposed to extract brain parenchymal regions. The image texture features of hemorrhagic area are firstly extracted by adaptive threshold segmentation algorithm, the precise location of hemorrhagic area is thereafter obtained through filtering the irrelevant physiological tissues such as soft tissue, brain tissue and cerebrospinal fluid. Finally, a three-dimensional structure of the hemorrhagic area is reconstructed based on interpolation to evaluate the amount of hemorrhage. A validation test with 500 patients from medical institution in Tianjin shows that the algorithm achieves a target recognition accuracy of 97.4% and could provide reference for the diagnosis of cerebral hemorrhage.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2019-146