Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle.
In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addi...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 5; pp. 898 - 909 |
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| Autor principal: | |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2013
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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=104229519&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104229519 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2013 vid: 26 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104229519 90397237 10.1007/s10278-013-9573-z NLM23354341 PMC3782609 104229519 ppf: 898 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle. aug: au: Mahapatra, Dwarikanath affil: Department of Computer Science, Swiss Federal Institute of Technology (ETH) Zurich, Room CAB F 61.1 Universitätstrasse 68092 Zurich Switzerland sug: subj: Magnetic Resonance Imaging Heart Ventricle, Left Heart Ventricle, Right Image Interpretation, Computer Assisted Methods Human Factor Analysis Myocardium Information Science Methods Algorithms T-Tests P-Value ab: In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a 'context penalty' that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method's robustness to noise and inaccurate segmentations. 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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