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作者

Chao Zhen Xu, Wenting

作者单位

f) College of Health Science, Yonsei University, Department of Radiation Convergence Engineering, Wonju, South Korea (GRID:grid.1544 30) (ISNI:0000 0004 0470 5454)  e) (ISNI:0000000119573309) ²  College of Health Science, Yonsei University, Department of Radiation Convergence Engineering, Wonju, South Korea (GRID:grid.1544 Chinese Academy of Sciences, Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Shenzhen, China (GRID:grid.922 ¹  Huaiyin District, College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China (GRID:grid.41058

刊名

Journal of Digital Imaging

年份

2021

卷号

Vol.34 No.5

页码

1264-1278

ISSN

0897-1889

摘要

Maximum intensity projection technology is a computer visualization method that projects three-dimensional spatial data on a visualization plane. According to the specific purposes, the specific lab thickness and direction can be selected. This technology can better show organs, such as blood vessels, arteries, veins, and bronchi and so forth, from different directions, which could bring more intuitive and comprehensive results for doctors in the diagnosis of related diseases. However, in this ...更多

Maximum intensity projection technology is a computer visualization method that projects three-dimensional spatial data on a visualization plane. According to the specific purposes, the specific lab thickness and direction can be selected. This technology can better show organs, such as blood vessels, arteries, veins, and bronchi and so forth, from different directions, which could bring more intuitive and comprehensive results for doctors in the diagnosis of related diseases. However, in this traditional projection technology, the details of the small projected target are not clearly visualized when the projected target is not much different from the surrounding environment, which could lead to missed diagnosis or misdiagnosis. Therefore, it is urgent to develop a new technology that can better and clearly display the angiogram. However, to the best of our knowledge, research in this area is scarce. To fill this gap in the literature, in the present study, we propose a new method based on the hybrid of convolutional neural network and radial basis function neural network to synthesize the projection image. We first adopted the U-net to obtain feature or enhanced images to be projected; subsequently, the RBF neural network performed further synthesis processing for these data; finally, the projection images were obtained. For experimental data, in order to increase the robustness of the proposed algorithm, the following three different types of datasets were adopted: the vascular projection of the brain, the bronchial projection of the lung parenchyma, and the vascular projection of the liver. In addition, radiologist evaluation and five classic metrics of image definition were implemented for effective analysis. Finally, compared to the traditional MIP technology and other structures, the use of a large number of different types of data and superior experimental results proved the versatility and robustness of the proposed method.收起

文献类型

期刊