Improvement of Partial Volume Segmentation for Brain Tissue on Diffusion Tensor Images Using Multiple-Tensor Estimation.
To improve evaluations of cortical and subcortical diffusivity in neurological diseases, it is necessary to improve the accuracy of brain diffusion tensor imaging (DTI) data segmentation. The conventional partial volume segmentation method fails to classify voxels with multiple white matter (WM) fib...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 6; pp. 1131 - 1141 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research Journal Article |
| Publicado: |
Springer Nature
Dec2013
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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=104153847&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104153847 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2013 vid: 26 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104153847 91842810 10.1007/s10278-013-9601-z NLM23589185 104153847 ppf: 1131 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Improvement of Partial Volume Segmentation for Brain Tissue on Diffusion Tensor Images Using Multiple-Tensor Estimation. aug: au: Kumazawa, Seiji Yoshiura, Takashi Honda, Hiroshi Toyofuku, Fukai affil: Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi Fukuoka 812-8582 Japan sug: subj: Brain Radiography Digital Imaging Methods Human Research Methodology Diagnostic Imaging Evaluation Funding Source ab: To improve evaluations of cortical and subcortical diffusivity in neurological diseases, it is necessary to improve the accuracy of brain diffusion tensor imaging (DTI) data segmentation. The conventional partial volume segmentation method fails to classify voxels with multiple white matter (WM) fiber orientations such as fiber-crossing regions. Our purpose was to improve the performance of segmentation by taking into account the partial volume effects due to both multiple tissue types and multiple WM fiber orientations. We quantitatively evaluated the overall performance of the proposed method using digital DTI phantom data. Moreover, we applied our method to human DTI data, and compared our results with those of a conventional method. In the phantom experiments, the conventional method and proposed method yielded almost the same root mean square error (RMSE) for gray matter (GM) and cerebrospinal fluid (CSF), while the RMSE in the proposed method was smaller than that in the conventional method for WM. The volume overlap measures between our segmentation results and the ground truth of the digital phantom were more than 0.8 in all three tissue types, and were greater than those in the conventional method. In visual comparisons for human data, the WM/GM/CSF regions obtained using our method were in better agreement with the corresponding regions depicted in the structural image than those obtained using the conventional method. The results of the digital phantom experiment and human data demonstrated that our method improved accuracy in the segmentation of brain tissue data on DTI compared to the conventional method. pubtype: Academic Journal doctype: diagnostic images equations & formulas research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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