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作者:

Zhang, Yan (Zhang, Yan.) | Zhang, Hui (Zhang, Hui.) | Zhuo, Li (Zhuo, Li.) | Li, Xiaoguang (Li, Xiaoguang.) | Zhao, Zhiyong (Zhao, Zhiyong.) | Zhao, Pengfei (Zhao, Pengfei.) | Wang, Zhenchang (Wang, Zhenchang.)

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EI Scopus

摘要:

For the quantitative analysis of medical images in clinical research, diagnosis and treatment, a reliable basic framework for developing a normal human ear atlas of voxel-based computed tomography (CT) images was proposed. We annotated 10 precise ear structures with different labels from 64 patients with normal ear structures. Paired-samples t test, Pearson’s test and descriptive statistics were carried on the volume and coordinate data, which were first obtained from annotation to verify the correlation and difference. In addition, we constructed a three dimensional (3D) model of the standard human ear atlas with six views for presentation. Through a series of statistical analyses, a standard 3D normal human ear atlas containing volume and spatial data was obtained from voxel-based CT images. There was a significant negative correlation exists between age and the volume of the incus, and no correlation with other structures. There was no significant correlation between slice thickness and the volume of 10 structures. The volume of most structures on both sides is significantly correlated and there was no significant difference in the volume of most structures on both sides except for the jugular foramen. Besides, the coordinate range of the bilateral structures is relatively consistent. The specific volume and spatial data for the human ear atlas are helpful in the diagnosis of abnormalities, and this 3D normal human ear atlas will provide new insights for radiologists in clinical research. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

关键词:

3D modeling Chemical analysis Clinical research Computerized tomography Diagnosis Image analysis Medical imaging

作者机构:

  • [ 1 ] [Zhang, Yan]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
  • [ 2 ] [Zhang, Yan]College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Zhang, Hui]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
  • [ 4 ] [Zhang, Hui]College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 5 ] [Zhuo, Li]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
  • [ 6 ] [Zhuo, Li]College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 7 ] [Li, Xiaoguang]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
  • [ 8 ] [Li, Xiaoguang]College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 9 ] [Zhao, Zhiyong]Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
  • [ 10 ] [Zhao, Pengfei]Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
  • [ 11 ] [Wang, Zhenchang]Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China

通讯作者信息:

  • [zhuo, li]college of microelectronics, faculty of information technology, beijing university of technology, beijing, china;;[zhuo, li]beijing key laboratory of computational intelligence and intelligent system, beijing university of technology, beijing, china

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来源 :

Sensing and Imaging

ISSN: 1557-2064

年份: 2019

期: 1

卷: 20

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 1

ESI高被引论文在榜: 0 展开所有

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