Two-Stage CNN Whole Heart Segmentation Combining Image Enhanced Attention Mechanism and Metric Classification.
Accurate segmentation of multiple tissues and organs in cardiac medical imaging is of great value in computer-aided cardiovascular diagnosis. However, it is challenging due to the complex distribution of various tissues and organs in cardiac MRI (magnetic resonance imaging) slices, low discriminativ...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 1; pp. 124 - 143 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2023
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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=162233260&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233260 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2023 vid: 36 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162233260 159395546 162233260 162233260 10.1007/s10278-022-00708-6 162233260 ppf: 124 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Two-Stage CNN Whole Heart Segmentation Combining Image Enhanced Attention Mechanism and Metric Classification. aug: au: Wang, Xuchu Wang, Fusheng Niu, Yanmin affil: Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, 400040, Chongqing, China sug: subj: Heart Function Tests Methods Magnetic Resonance Imaging Methods Image Enhancement Neural Networks (Computer) Human Female Male Deep Learning Conceptual Framework Image Interpretation, Computer Assisted Spatial Perception Image Processing, Computer Assisted Descriptive Statistics Funding Source Female Male ab: Accurate segmentation of multiple tissues and organs in cardiac medical imaging is of great value in computer-aided cardiovascular diagnosis. However, it is challenging due to the complex distribution of various tissues and organs in cardiac MRI (magnetic resonance imaging) slices, low discriminative and large spanning organs. To handle these problems, a two-stage CNN (convolutional neural network) segmentation method based on the combination of Log-Gabor filter attention mechanism and metric classification is proposed. The Log-Gabor filterbank is applied to selectively enhance the texture information and contour information of each tissue and organ, and the spatial and channel attention mechanism jointly with the varying kernel size of Log-Gabor filterbank is incorporated into the codec structure to adaptively extract target features of different sizes and focus on the discriminative features in the network. To solve the problem of insufficient segmentation on subtle and adherent edges involving different tissues, a metric classification network is incorporated to finely optimize the hard-to-be-segmented boundaries. The proposed method was tested on cardiac MRI data set to segment 7 cardiac tissues, and the rationality and effectiveness of the method were verified. In comparison to a series of deep learning-based segmentation models, the proposed method achieves competitive performance. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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