A Probabilistic Patch-Based Label Fusion Model for Multi-Atlas Segmentation With Registration Refinement: Application to Cardiac MR Images
The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of th...
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| Published in | IEEE transactions on medical imaging Vol. 32; no. 7; pp. 1302 - 1315 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.07.2013
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| Subjects | |
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| Abstract | The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of the expert observer. In this paper, a multi-atlas method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves image registration accuracy by utilizing label information, which leads to improvement of segmentation accuracy. The proposed method was evaluated on a cardiac MR image set of 28 subjects. The average Dice overlap metric of our segmentation is 0.92 for the LV cavity, 0.89 for the right ventricular cavity and 0.82 for the myocardium. The results show that the proposed method is able to provide accurate information for clinical diagnosis. |
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| AbstractList | The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of the expert observer. In this paper, a multi-atlas method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves image registration accuracy by utilizing label information, which leads to improvement of segmentation accuracy. The proposed method was evaluated on a cardiac MR image set of 28 subjects. The average Dice overlap metric of our segmentation is 0.92 for the LV cavity, 0.89 for the right ventricular cavity and 0.82 for the myocardium. The results show that the proposed method is able to provide accurate information for clinical diagnosis.The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of the expert observer. In this paper, a multi-atlas method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves image registration accuracy by utilizing label information, which leads to improvement of segmentation accuracy. The proposed method was evaluated on a cardiac MR image set of 28 subjects. The average Dice overlap metric of our segmentation is 0.92 for the LV cavity, 0.89 for the right ventricular cavity and 0.82 for the myocardium. The results show that the proposed method is able to provide accurate information for clinical diagnosis. The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of the expert observer. In this paper, a multi-atlas method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves image registration accuracy by utilizing label information, which leads to improvement of segmentation accuracy. The proposed method was evaluated on a cardiac MR image set of 28 subjects. The average Dice overlap metric of our segmentation is 0.92 for the LV cavity, 0.89 for the right ventricular cavity and 0.82 for the myocardium. The results show that the proposed method is able to provide accurate information for clinical diagnosis. |
| Author | Jamil-Copley, S. Peters, N. S. Wenjia Bai O'Regan, D. P. Tong Tong Rueckert, D. Wenzhe Shi Haiyan Wang |
| Author_xml | – sequence: 1 surname: Wenjia Bai fullname: Wenjia Bai organization: Dept. of Comput., Imperial Coll. London, London, UK – sequence: 2 surname: Wenzhe Shi fullname: Wenzhe Shi organization: Dept. of Comput., Imperial Coll. London, London, UK – sequence: 3 givenname: D. P. surname: O'Regan fullname: O'Regan, D. P. organization: MRC Clinical Sci. Centre, Imperial Coll. London, London, UK – sequence: 4 surname: Tong Tong fullname: Tong Tong organization: Dept. of Comput., Imperial Coll. London, London, UK – sequence: 5 surname: Haiyan Wang fullname: Haiyan Wang organization: Dept. of Comput., Imperial Coll. London, London, UK – sequence: 6 givenname: S. surname: Jamil-Copley fullname: Jamil-Copley, S. organization: Cardiology Dept., Imperial Coll. Healthcare NHS Trust, London, UK – sequence: 7 givenname: N. S. surname: Peters fullname: Peters, N. S. email: n.peters@imperial.ac.uk organization: Cardiology Dept., Imperial Coll. Healthcare NHS Trust, London, UK – sequence: 8 givenname: D. surname: Rueckert fullname: Rueckert, D. organization: Dept. of Comput., Imperial Coll. London, London, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23568495$$D View this record in MEDLINE/PubMed |
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| References | fonov (ref39) 2012 ref57 ref13 ref56 ref12 ref59 ref15 ref58 ref14 zhu (ref7) 2010; 29 suinesiaputra (ref10) 2011 ref52 ref55 ref54 risholm (ref46) 2010 ref17 van rikxoort (ref19) 2010; 14 ref16 radau (ref9) 2009 ref18 ref50 petitjean (ref11) 2012 ref45 ref48 risholm (ref47) 2010 ref42 ref41 ref44 ref43 ref49 ref8 tong (ref32) 2012 coup (ref25) 2010 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref1 d'agostino (ref53) 2006 ref24 ref23 ref26 ref20 wolz (ref38) 0 ref22 ref21 ref28 ref27 ref29 jolly (ref2) 2009 lynch (ref4) 2008; 27 chen (ref51) 2005 |
| References_xml | – start-page: 10 year: 0 ident: ref38 publication-title: MICCAI 2012 – ident: ref57 doi: 10.1016/S0002-9149(02)02381-0 – ident: ref58 doi: 10.1109/42.836373 – ident: ref36 doi: 10.1007/978-3-642-23626-6_79 – ident: ref29 doi: 10.1109/TMI.2007.906087 – ident: ref52 doi: 10.1016/j.media.2005.03.004 – ident: ref35 doi: 10.1109/TPAMI.2012.143 – ident: ref21 doi: 10.1109/TMI.2008.2011480 – ident: ref8 doi: 10.1109/TMI.2010.2047112 – ident: ref26 doi: 10.1016/j.neuroimage.2010.09.018 – ident: ref12 doi: 10.1016/j.acra.2012.03.022 – start-page: 88 year: 2011 ident: ref10 article-title: Left ventricular segmentation challenge from cardiac MRI: A collation study publication-title: MICCAI Cardiac MR LV Segmentation Challenge – ident: ref50 doi: 10.1016/j.neuroimage.2005.02.018 – year: 2012 ident: ref11 article-title: Right ventricle segmentation in cardiac MRI: A MICCAI12 challenge publication-title: MICCAI 2012 Right Ventricle Segmentation Challenge Workshop – ident: ref6 doi: 10.1109/TMI.2009.2030799 – ident: ref40 doi: 10.1016/j.neuroimage.2011.09.012 – ident: ref17 doi: 10.1016/j.neuroimage.2009.02.018 – start-page: 126 year: 2005 ident: ref51 publication-title: Information Processing in Medical Imaging – start-page: 104 year: 2010 ident: ref46 article-title: Bayesian estimation of deformation and elastic parameters in non-rigid registration publication-title: Biomed Image Registrat doi: 10.1007/978-3-642-14366-3_10 – ident: ref48 doi: 10.1016/j.neuroimage.2011.09.002 – ident: ref33 doi: 10.1007/978-3-642-33530-3_8 – ident: ref54 doi: 10.1109/42.796284 – ident: ref34 doi: 10.1016/j.neuroimage.2011.01.006 – ident: ref1 doi: 10.1016/j.imavis.2010.01.001 – ident: ref13 doi: 10.1016/j.neuroimage.2006.05.061 – ident: ref56 doi: 10.1109/TMI.2002.804426 – ident: ref44 doi: 10.1016/j.media.2012.10.002 – ident: ref49 doi: 10.1109/TMI.2012.2236651 – start-page: 129 year: 2010 ident: ref25 publication-title: Medical Image Computing and Computer Assisted Intervention – ident: ref3 doi: 10.1016/j.media.2004.06.005 – ident: ref27 doi: 10.1007/s11263-009-0272-7 – volume: 27 start-page: 195 year: 2008 ident: ref4 article-title: Segmentation of the left ventricle of the heart in 3-<formula formulatype="inline"><tex Notation="TeX">${\rm D}+{\rm t}$</tex></formula> MRI data using an optimized nonrigid temporal model publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2007.904681 – year: 2009 ident: ref9 article-title: Evaluation framework for algorithms segmenting short axis cardiac MRI publication-title: MICCAI Workshop Cardiac MR LV Seg Chall – year: 2009 ident: ref2 article-title: Fully automatic left ventricle segmentation in cardiac cine MR images using registration and minimum surfaces publication-title: The MIDAS Journal - Cardiac MR Left Ventricle Segmentation Challenge – start-page: 554 year: 2010 ident: ref47 article-title: Summarizing and visualizing uncertainty in non-rigid registration publication-title: Proc Med Image Comput Computer-Assist Intervent – ident: ref23 doi: 10.1109/TMI.2010.2050897 – start-page: 63 year: 2012 ident: ref39 article-title: Multi-atlas labeling with population-specific template and non-local patchbased label fusion publication-title: Proc MICCAI 2012 Grand Challenge Workshop Multi-Atlas Label – ident: ref42 doi: 10.1007/978-3-642-33530-3_10 – ident: ref20 doi: 10.1109/TMI.2009.2014372 – year: 2012 ident: ref32 article-title: Segmentation of brain MR images via sparse patch representation publication-title: MICCAI Workshop Sparsity Techniques in Medical Imaging – ident: ref43 doi: 10.1007/978-3-642-33454-2_53 – volume: 14 start-page: 39 year: 2010 ident: ref19 article-title: Adaptive local multi-atlas segmentation: Application to the heart and the caudate nucleus publication-title: Med Image Anal doi: 10.1016/j.media.2009.10.001 – ident: ref30 doi: 10.1109/TMI.2011.2156806 – ident: ref59 doi: 10.1007/3-540-45631-7_39 – volume: 29 start-page: 669 year: 2010 ident: ref7 article-title: Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2009.2031063 – ident: ref55 doi: 10.1109/TMI.2003.815867 – ident: ref37 doi: 10.1002/hbm.22183 – ident: ref14 doi: 10.1109/TMI.2004.830803 – ident: ref18 doi: 10.1016/j.neuroimage.2009.10.026 – ident: ref45 doi: 10.1002/mrm.1910340618 – ident: ref31 doi: 10.1109/CVPR.2011.5995382 – ident: ref24 doi: 10.1109/TMI.2004.828354 – ident: ref22 doi: 10.1109/TMI.2010.2057442 – ident: ref16 doi: 10.1016/j.neuroimage.2004.10.017 – ident: ref28 doi: 10.1007/s11263-007-0052-1 – ident: ref5 doi: 10.1016/j.media.2010.12.004 – start-page: 136 year: 2006 ident: ref53 publication-title: 2nd Int Workshop Biomedical Image Registration doi: 10.1007/11784012_17 – ident: ref15 doi: 10.1016/j.neuroimage.2003.11.010 – ident: ref41 doi: 10.1109/ISBI.2012.6235691 |
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| SubjectTerms | Accuracy Algorithms Bayes methods Bayes Theorem Gaussian distribution Heart - anatomy & histology Heart Ventricles - anatomy & histology Humans Image Processing, Computer-Assisted - methods Image registration Image segmentation Magnetic Resonance Imaging - methods multi-atlas segmentation patch-based segmentation Probabilistic logic Vectors |
| Title | A Probabilistic Patch-Based Label Fusion Model for Multi-Atlas Segmentation With Registration Refinement: Application to Cardiac MR Images |
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