White Matter Segmentation Algorithm for DTI Images Based on Super-Pixel Full Convolutional Network.
Diffusion tensor imaging (DTI) is a new imaging method that can be used to non-invasively measure the diffusion coefficient of water molecules in biological tissue structures in recent years. Since the DTI data is a tensor space, its segmentation is different from ordinary MRI images. Based on the e...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 9 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2019
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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=138200117&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138200117 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2019 vid: 43 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138200117 138200117 138200117 10.1007/s10916-019-1431-1 138200117 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: White Matter Segmentation Algorithm for DTI Images Based on Super-Pixel Full Convolutional Network. aug: au: Mu, Yiping Li, Qi Zhang, Yang affil: Central Hospital Affiliated of Shenyang Medical College, 110024, Shenyang, Liaoning, China sug: subj: White Matter Brain Physiopathology Magnetic Resonance Imaging Methods Algorithms Image Interpretation, Computer Assisted Neural Networks (Computer) Methods Human Models, Statistical Qualitative Studies Quantitative Studies Funding Source ab: Diffusion tensor imaging (DTI) is a new imaging method that can be used to non-invasively measure the diffusion coefficient of water molecules in biological tissue structures in recent years. Since the DTI data is a tensor space, its segmentation is different from ordinary MRI images. Based on the existing deep learning model, an improved image semantic segmentation method based on super-pixels and conditional random field is proposed. Firstly, this paper uses the existing feature extraction model based on deep learning to obtain rough semantic segmentation results, including high-level semantic information of the image but lacking details of the image. In addition, the super-pixel segmentation algorithm is implemented to obtain super-pixels that carries more low-level information. Secondly, due to the lack of image details in rough segmentation results, the segmentation of the edge of the image is inaccurate. In this paper, a boundary optimization algorithm is proposed to optimize the edge segmentation accuracy of the rough results. Finally, the use of super-pixels for local boundary optimization can improve the segmentation accuracy. Experiments results show that this segment is a practical and effective method. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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