A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network.
Maximum intensity projection (MIP) 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...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 5; pp. 1264 - 1279 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153241274&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153241274 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2021 vid: 34 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153241274 152363305 153241274 153241274 10.1007/s10278-021-00504-8 153241274 ppf: 1264 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network. aug: au: Chao, Zhen Xu, Wenting affil: College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Huaiyin District, 6699 Qingdao Road, 250117, Jinan, Shandong, China sug: subj: Neural Networks (Computer) Digital Technology Magnetic Resonance Angiography Methods Imaging, Three-Dimensional Methods Algorithms Vascular Diseases Diagnosis Human Brain Blood Supply Lung Blood Supply Liver Blood Supply Bronchi Blood Supply Sensitivity and Specificity ab: Maximum intensity projection (MIP) 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 (CNN) and radial basis function neural network (RBFNN) 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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