4D Shape Priors for a Level Set Segmentation of the Left Myocardium in SPECT Sequences
We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In con...
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| creator | Kohlberger, Timo Cremers, Daniel Rousson, Mikaël Ramaraj, Ramamani Funka-Lea, Gareth |
| description | We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. Experimental results on the segmentation of SPECT sequences of the left myocardium confirm that the 4D shape model outperforms respective 3D models, because it takes into account a statistical model of the temporal shape evolution. |
| doi_str_mv | 10.1007/11866565_12 |
| format | Book Chapter |
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To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. 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To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. Experimental results on the segmentation of SPECT sequences of the left myocardium confirm that the 4D shape model outperforms respective 3D models, because it takes into account a statistical model of the temporal shape evolution.</description><subject>Active Contour</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computer Simulation</subject><subject>Electrocardiography - methods</subject><subject>Heart Ventricles - diagnostic imaging</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Models, Cardiovascular</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Point Correspondence</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Shape Model</subject><subject>SPECT Sequence</subject><subject>Statistical Shape</subject><subject>Subtraction Technique</subject><subject>Time Factors</subject><subject>Tomography, Emission-Computed, Single-Photon - methods</subject><subject>Ventricular Dysfunction, Left - diagnostic imaging</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540447075</isbn><isbn>9783540447078</isbn><isbn>3540447083</isbn><isbn>9783540447085</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2006</creationdate><recordtype>book_chapter</recordtype><sourceid>EIF</sourceid><recordid>eNpNkMtOwzAQRc1LtJSu2CNvWQQ8sWM7SxTKQyqiUiu2kZ1M2kATFztF6t8TVBCMdDWLczTSXEIugF0DY-oGQEuZyCSH-ICc8UQwIRTT_JAMQQJEnIv06A-o5JgMGWdxlCrBB2Qcwhvrh4PWKj0lA1C9qpUekldxR-crs0E687XzgVbOU0On-IlrOseuz7LBtjNd7VrqKtqtsKdVR593rjC-rLcNrVs6n02yRS9_bLEtMJyTk8qsA45_9ogs7ieL7DGavjw8ZbfTqOASuggZShCCgbBWAJaVEcqYRAurrIyxiNNCaABeGpairYpYK5GaWBnLlVGGj8jl_uxmaxss842vG-N3-e9_vXC1F0KP2iX63Dr3HnJg-Xex-b9i-Rf2GGGl</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Kohlberger, Timo</creator><creator>Cremers, Daniel</creator><creator>Rousson, Mikaël</creator><creator>Ramaraj, Ramamani</creator><creator>Funka-Lea, Gareth</creator><general>Springer Berlin Heidelberg</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope></search><sort><creationdate>2006</creationdate><title>4D Shape Priors for a Level Set Segmentation of the Left Myocardium in SPECT Sequences</title><author>Kohlberger, Timo ; Cremers, Daniel ; Rousson, Mikaël ; Ramaraj, Ramamani ; Funka-Lea, Gareth</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-e0e6144014bb41edfa47aa584b7b62ec29c48113da09ebfc28749a27ab37a7a3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Active Contour</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Computer Simulation</topic><topic>Electrocardiography - methods</topic><topic>Heart Ventricles - diagnostic imaging</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Models, Cardiovascular</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Point Correspondence</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Shape Model</topic><topic>SPECT Sequence</topic><topic>Statistical Shape</topic><topic>Subtraction Technique</topic><topic>Time Factors</topic><topic>Tomography, Emission-Computed, Single-Photon - methods</topic><topic>Ventricular Dysfunction, Left - diagnostic imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kohlberger, Timo</creatorcontrib><creatorcontrib>Cremers, Daniel</creatorcontrib><creatorcontrib>Rousson, Mikaël</creatorcontrib><creatorcontrib>Ramaraj, Ramamani</creatorcontrib><creatorcontrib>Funka-Lea, Gareth</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kohlberger, Timo</au><au>Cremers, Daniel</au><au>Rousson, Mikaël</au><au>Ramaraj, Ramamani</au><au>Funka-Lea, Gareth</au><au>Larsen, Rasmus</au><au>Sporring, Jon</au><au>Nielsen, Mads</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>4D Shape Priors for a Level Set Segmentation of the Left Myocardium in SPECT Sequences</atitle><btitle>Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006</btitle><addtitle>Med Image Comput Comput Assist Interv</addtitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2006</date><risdate>2006</risdate><volume>9</volume><issue>Pt 1</issue><spage>92</spage><epage>100</epage><pages>92-100</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540447075</isbn><isbn>9783540447078</isbn><eisbn>3540447083</eisbn><eisbn>9783540447085</eisbn><abstract>We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. Experimental results on the segmentation of SPECT sequences of the left myocardium confirm that the 4D shape model outperforms respective 3D models, because it takes into account a statistical model of the temporal shape evolution.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>17354878</pmid><doi>10.1007/11866565_12</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006, 2006, Vol.9 (Pt 1), p.92-100 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_springer_books_10_1007_11866565_12 |
| source | MEDLINE; Springer Books |
| subjects | Active Contour Algorithms Artificial Intelligence Computer Simulation Electrocardiography - methods Heart Ventricles - diagnostic imaging Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Models, Cardiovascular Pattern Recognition, Automated - methods Point Correspondence Reproducibility of Results Sensitivity and Specificity Shape Model SPECT Sequence Statistical Shape Subtraction Technique Time Factors Tomography, Emission-Computed, Single-Photon - methods Ventricular Dysfunction, Left - diagnostic imaging |
| title | 4D Shape Priors for a Level Set Segmentation of the Left Myocardium in SPECT Sequences |
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