U-Net Based Intracranial Hemorrhage Recognition

doi:10.13190/j.jbupt.2019-146

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (3): 92-98.doi: 10.13190/j.jbupt.2019-146

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U-Net Based Intracranial Hemorrhage Recognition

ZHANG Tian-qi¹, KANG Bo^2,1, MENG Xiang-fei¹, LIU Yi-lin³, ZHOU Ying³

1. National Supercomputer Center in Tianjin, Tianjin 300457, China;
2. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
3. Peking University Binhai Hospital, Tianjin 300450, China

Received:2019-07-09 Online:2020-06-28 Published:2020-06-24
Supported by:

Abstract

Abstract: 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.

Key words: deep learning, image segmentation, cerebral hemorrhage, craniocerebral computed tomography

CLC Number:

TN319

ZHANG Tian-qi, KANG Bo, MENG Xiang-fei, LIU Yi-lin, ZHOU Ying. U-Net Based Intracranial Hemorrhage Recognition[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 92-98.

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U-Net Based Intracranial Hemorrhage Recognition

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