Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models
Abstract We evaluate in this paper different strategies for the construction of a statistical shape model (SSM) of the left ventricle (LV) to be used for segmentation in cardiac magnetic resonance (CMR) images. From a large database of LV surfaces obtained throughout the cardiac cycle from 3D echoca...
Gespeichert in:
| Veröffentlicht in: | Journal of electrocardiology 2016-05, Vol.49 (3), p.383-391 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 391 |
|---|---|
| container_issue | 3 |
| container_start_page | 383 |
| container_title | Journal of electrocardiology |
| container_volume | 49 |
| creator | Piazzese, Concetta Carminati, M. Chiara Colombo, Andrea Krause, Rolf Potse, Mark Auricchio, Angelo Weinert, Lynn Tamborini, Gloria Pepi, Mauro Lang, Roberto M Caiani, Enrico G |
| description | Abstract We evaluate in this paper different strategies for the construction of a statistical shape model (SSM) of the left ventricle (LV) to be used for segmentation in cardiac magnetic resonance (CMR) images. From a large database of LV surfaces obtained throughout the cardiac cycle from 3D echocardiographic (3DE) LV images, different LV shape models were built by varying the considered phase in the cardiac cycle and the registration procedure employed for surface alignment. Principal component analysis was computed to describe the statistical variability of the SSMs, which were then deformed by applying an active shape model (ASM) approach to segment the LV endocardium in CMR images of 45 patients. Segmentation performance was evaluated by comparing LV volumes derived by ASM segmentation with different SSMs and those obtained by manual tracing, considered as a reference. A high correlation (r2 > 0.92) was found in all cases, with better results when using the SSM models comprising more than one frame of the cardiac cycle. |
| doi_str_mv | 10.1016/j.jelectrocard.2016.03.017 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_01302237v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>1_s2_0_S0022073616000984</els_id><sourcerecordid>1786525363</sourcerecordid><originalsourceid>FETCH-LOGICAL-c521t-b96e832b3e0a1e019de7a6829a7f33bab061baeabf6a83cd12a0a749f9f654983</originalsourceid><addsrcrecordid>eNqNUk2P0zAUjBCILQt_AVmc2EPCs93YCQek1fKxSJU4LJwtx3luXRK72Emlij-PQ5cV4sTJ0mjejDUzRfGKQkWBijf7ao8DmikGo2NfsYxVwCug8lGxojVnZbPm8LhYATBWguTioniW0h4AWibZ0-KCSVgLCrAqft7hdkQ_6ckFT4Il0w7JgHYix4xGZ-ZBR4K-_-3l5pHYGEYy6q3HyRkSMQWvvUHiMoaJdCcyJ-e3pHfWYswiJC3qKbP1QNJOH5CMocchPS-eWD0kfHH_XhbfPn74enNbbr58-nxzvSlNzehUdq3AhrOOI2iKQNsepRYNa7W0nHe6A0E7jbqzQjfc9JRp0HLd2taKet02_LK4Ouvu9KAOMX80nlTQTt1eb9SCAeU5KS6PNHNfn7mHGH7MmCY1umRwGLTHMCdFZSNqVnPBM_XtmWpiSCmifdCmoJai1F79XZRailLAs53Mxy_vfeZuxP7h9E8zmfD-TMhB4dFhVMk4zEH3LmZJ1Qf3fz7v_pExg_NLFd_xhGkf5uhz9oqqxBSou2Uyy2KoWNaSd_QLmd7CTA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1786525363</pqid></control><display><type>article</type><title>Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Piazzese, Concetta ; Carminati, M. Chiara ; Colombo, Andrea ; Krause, Rolf ; Potse, Mark ; Auricchio, Angelo ; Weinert, Lynn ; Tamborini, Gloria ; Pepi, Mauro ; Lang, Roberto M ; Caiani, Enrico G</creator><creatorcontrib>Piazzese, Concetta ; Carminati, M. Chiara ; Colombo, Andrea ; Krause, Rolf ; Potse, Mark ; Auricchio, Angelo ; Weinert, Lynn ; Tamborini, Gloria ; Pepi, Mauro ; Lang, Roberto M ; Caiani, Enrico G</creatorcontrib><description>Abstract We evaluate in this paper different strategies for the construction of a statistical shape model (SSM) of the left ventricle (LV) to be used for segmentation in cardiac magnetic resonance (CMR) images. From a large database of LV surfaces obtained throughout the cardiac cycle from 3D echocardiographic (3DE) LV images, different LV shape models were built by varying the considered phase in the cardiac cycle and the registration procedure employed for surface alignment. Principal component analysis was computed to describe the statistical variability of the SSMs, which were then deformed by applying an active shape model (ASM) approach to segment the LV endocardium in CMR images of 45 patients. Segmentation performance was evaluated by comparing LV volumes derived by ASM segmentation with different SSMs and those obtained by manual tracing, considered as a reference. A high correlation (r2 > 0.92) was found in all cases, with better results when using the SSM models comprising more than one frame of the cardiac cycle.</description><identifier>ISSN: 0022-0736</identifier><identifier>EISSN: 1532-8430</identifier><identifier>DOI: 10.1016/j.jelectrocard.2016.03.017</identifier><identifier>PMID: 27046100</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Cardiac MRI ; Cardiology and cardiovascular system ; Cardiovascular ; Computer Simulation ; Echocardiography - methods ; Echocardiography, Three-Dimensional - methods ; Endocardium - diagnostic imaging ; Endocardium - pathology ; Female ; Human health and pathology ; Humans ; Image segmentation ; Left ventricular volume ; Life Sciences ; Magnetic Resonance Imaging, Cine - methods ; Male ; Middle Aged ; Models, Anatomic ; Models, Cardiovascular ; Models, Statistical ; Reproducibility of Results ; Sensitivity and Specificity ; Statistical shape model ; Subtraction Technique ; Ventricular Dysfunction, Left - diagnostic imaging ; Ventricular Dysfunction, Left - pathology</subject><ispartof>Journal of electrocardiology, 2016-05, Vol.49 (3), p.383-391</ispartof><rights>2016 Elsevier Inc.</rights><rights>Copyright © 2016 Elsevier Inc. All rights reserved.</rights><rights>Attribution - NonCommercial - ShareAlike</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c521t-b96e832b3e0a1e019de7a6829a7f33bab061baeabf6a83cd12a0a749f9f654983</citedby><cites>FETCH-LOGICAL-c521t-b96e832b3e0a1e019de7a6829a7f33bab061baeabf6a83cd12a0a749f9f654983</cites><orcidid>0000-0002-3605-1809 ; 0000-0001-5408-5271 ; 0000-0003-4166-2687 ; 0000-0003-2116-6993</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jelectrocard.2016.03.017$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3536,27903,27904,45974</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27046100$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://inria.hal.science/hal-01302237$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Piazzese, Concetta</creatorcontrib><creatorcontrib>Carminati, M. Chiara</creatorcontrib><creatorcontrib>Colombo, Andrea</creatorcontrib><creatorcontrib>Krause, Rolf</creatorcontrib><creatorcontrib>Potse, Mark</creatorcontrib><creatorcontrib>Auricchio, Angelo</creatorcontrib><creatorcontrib>Weinert, Lynn</creatorcontrib><creatorcontrib>Tamborini, Gloria</creatorcontrib><creatorcontrib>Pepi, Mauro</creatorcontrib><creatorcontrib>Lang, Roberto M</creatorcontrib><creatorcontrib>Caiani, Enrico G</creatorcontrib><title>Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models</title><title>Journal of electrocardiology</title><addtitle>J Electrocardiol</addtitle><description>Abstract We evaluate in this paper different strategies for the construction of a statistical shape model (SSM) of the left ventricle (LV) to be used for segmentation in cardiac magnetic resonance (CMR) images. From a large database of LV surfaces obtained throughout the cardiac cycle from 3D echocardiographic (3DE) LV images, different LV shape models were built by varying the considered phase in the cardiac cycle and the registration procedure employed for surface alignment. Principal component analysis was computed to describe the statistical variability of the SSMs, which were then deformed by applying an active shape model (ASM) approach to segment the LV endocardium in CMR images of 45 patients. Segmentation performance was evaluated by comparing LV volumes derived by ASM segmentation with different SSMs and those obtained by manual tracing, considered as a reference. A high correlation (r2 > 0.92) was found in all cases, with better results when using the SSM models comprising more than one frame of the cardiac cycle.</description><subject>Cardiac MRI</subject><subject>Cardiology and cardiovascular system</subject><subject>Cardiovascular</subject><subject>Computer Simulation</subject><subject>Echocardiography - methods</subject><subject>Echocardiography, Three-Dimensional - methods</subject><subject>Endocardium - diagnostic imaging</subject><subject>Endocardium - pathology</subject><subject>Female</subject><subject>Human health and pathology</subject><subject>Humans</subject><subject>Image segmentation</subject><subject>Left ventricular volume</subject><subject>Life Sciences</subject><subject>Magnetic Resonance Imaging, Cine - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Models, Anatomic</subject><subject>Models, Cardiovascular</subject><subject>Models, Statistical</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Statistical shape model</subject><subject>Subtraction Technique</subject><subject>Ventricular Dysfunction, Left - diagnostic imaging</subject><subject>Ventricular Dysfunction, Left - pathology</subject><issn>0022-0736</issn><issn>1532-8430</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNUk2P0zAUjBCILQt_AVmc2EPCs93YCQek1fKxSJU4LJwtx3luXRK72Emlij-PQ5cV4sTJ0mjejDUzRfGKQkWBijf7ao8DmikGo2NfsYxVwCug8lGxojVnZbPm8LhYATBWguTioniW0h4AWibZ0-KCSVgLCrAqft7hdkQ_6ckFT4Il0w7JgHYix4xGZ-ZBR4K-_-3l5pHYGEYy6q3HyRkSMQWvvUHiMoaJdCcyJ-e3pHfWYswiJC3qKbP1QNJOH5CMocchPS-eWD0kfHH_XhbfPn74enNbbr58-nxzvSlNzehUdq3AhrOOI2iKQNsepRYNa7W0nHe6A0E7jbqzQjfc9JRp0HLd2taKet02_LK4Ouvu9KAOMX80nlTQTt1eb9SCAeU5KS6PNHNfn7mHGH7MmCY1umRwGLTHMCdFZSNqVnPBM_XtmWpiSCmifdCmoJai1F79XZRailLAs53Mxy_vfeZuxP7h9E8zmfD-TMhB4dFhVMk4zEH3LmZJ1Qf3fz7v_pExg_NLFd_xhGkf5uhz9oqqxBSou2Uyy2KoWNaSd_QLmd7CTA</recordid><startdate>20160501</startdate><enddate>20160501</enddate><creator>Piazzese, Concetta</creator><creator>Carminati, M. Chiara</creator><creator>Colombo, Andrea</creator><creator>Krause, Rolf</creator><creator>Potse, Mark</creator><creator>Auricchio, Angelo</creator><creator>Weinert, Lynn</creator><creator>Tamborini, Gloria</creator><creator>Pepi, Mauro</creator><creator>Lang, Roberto M</creator><creator>Caiani, Enrico G</creator><general>Elsevier Inc</general><general>Elsevier</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-3605-1809</orcidid><orcidid>https://orcid.org/0000-0001-5408-5271</orcidid><orcidid>https://orcid.org/0000-0003-4166-2687</orcidid><orcidid>https://orcid.org/0000-0003-2116-6993</orcidid></search><sort><creationdate>20160501</creationdate><title>Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models</title><author>Piazzese, Concetta ; Carminati, M. Chiara ; Colombo, Andrea ; Krause, Rolf ; Potse, Mark ; Auricchio, Angelo ; Weinert, Lynn ; Tamborini, Gloria ; Pepi, Mauro ; Lang, Roberto M ; Caiani, Enrico G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c521t-b96e832b3e0a1e019de7a6829a7f33bab061baeabf6a83cd12a0a749f9f654983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Cardiac MRI</topic><topic>Cardiology and cardiovascular system</topic><topic>Cardiovascular</topic><topic>Computer Simulation</topic><topic>Echocardiography - methods</topic><topic>Echocardiography, Three-Dimensional - methods</topic><topic>Endocardium - diagnostic imaging</topic><topic>Endocardium - pathology</topic><topic>Female</topic><topic>Human health and pathology</topic><topic>Humans</topic><topic>Image segmentation</topic><topic>Left ventricular volume</topic><topic>Life Sciences</topic><topic>Magnetic Resonance Imaging, Cine - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Models, Anatomic</topic><topic>Models, Cardiovascular</topic><topic>Models, Statistical</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Statistical shape model</topic><topic>Subtraction Technique</topic><topic>Ventricular Dysfunction, Left - diagnostic imaging</topic><topic>Ventricular Dysfunction, Left - pathology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Piazzese, Concetta</creatorcontrib><creatorcontrib>Carminati, M. Chiara</creatorcontrib><creatorcontrib>Colombo, Andrea</creatorcontrib><creatorcontrib>Krause, Rolf</creatorcontrib><creatorcontrib>Potse, Mark</creatorcontrib><creatorcontrib>Auricchio, Angelo</creatorcontrib><creatorcontrib>Weinert, Lynn</creatorcontrib><creatorcontrib>Tamborini, Gloria</creatorcontrib><creatorcontrib>Pepi, Mauro</creatorcontrib><creatorcontrib>Lang, Roberto M</creatorcontrib><creatorcontrib>Caiani, Enrico G</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Journal of electrocardiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Piazzese, Concetta</au><au>Carminati, M. Chiara</au><au>Colombo, Andrea</au><au>Krause, Rolf</au><au>Potse, Mark</au><au>Auricchio, Angelo</au><au>Weinert, Lynn</au><au>Tamborini, Gloria</au><au>Pepi, Mauro</au><au>Lang, Roberto M</au><au>Caiani, Enrico G</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models</atitle><jtitle>Journal of electrocardiology</jtitle><addtitle>J Electrocardiol</addtitle><date>2016-05-01</date><risdate>2016</risdate><volume>49</volume><issue>3</issue><spage>383</spage><epage>391</epage><pages>383-391</pages><issn>0022-0736</issn><eissn>1532-8430</eissn><abstract>Abstract We evaluate in this paper different strategies for the construction of a statistical shape model (SSM) of the left ventricle (LV) to be used for segmentation in cardiac magnetic resonance (CMR) images. From a large database of LV surfaces obtained throughout the cardiac cycle from 3D echocardiographic (3DE) LV images, different LV shape models were built by varying the considered phase in the cardiac cycle and the registration procedure employed for surface alignment. Principal component analysis was computed to describe the statistical variability of the SSMs, which were then deformed by applying an active shape model (ASM) approach to segment the LV endocardium in CMR images of 45 patients. Segmentation performance was evaluated by comparing LV volumes derived by ASM segmentation with different SSMs and those obtained by manual tracing, considered as a reference. A high correlation (r2 > 0.92) was found in all cases, with better results when using the SSM models comprising more than one frame of the cardiac cycle.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>27046100</pmid><doi>10.1016/j.jelectrocard.2016.03.017</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-3605-1809</orcidid><orcidid>https://orcid.org/0000-0001-5408-5271</orcidid><orcidid>https://orcid.org/0000-0003-4166-2687</orcidid><orcidid>https://orcid.org/0000-0003-2116-6993</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0022-0736 |
| ispartof | Journal of electrocardiology, 2016-05, Vol.49 (3), p.383-391 |
| issn | 0022-0736 1532-8430 |
| language | eng |
| recordid | cdi_hal_primary_oai_HAL_hal_01302237v1 |
| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Cardiac MRI Cardiology and cardiovascular system Cardiovascular Computer Simulation Echocardiography - methods Echocardiography, Three-Dimensional - methods Endocardium - diagnostic imaging Endocardium - pathology Female Human health and pathology Humans Image segmentation Left ventricular volume Life Sciences Magnetic Resonance Imaging, Cine - methods Male Middle Aged Models, Anatomic Models, Cardiovascular Models, Statistical Reproducibility of Results Sensitivity and Specificity Statistical shape model Subtraction Technique Ventricular Dysfunction, Left - diagnostic imaging Ventricular Dysfunction, Left - pathology |
| title | Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T15%3A20%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Segmentation%20of%20the%20left%20ventricular%20endocardium%20from%20magnetic%20resonance%20images%20by%20using%20different%20statistical%20shape%20models&rft.jtitle=Journal%20of%20electrocardiology&rft.au=Piazzese,%20Concetta&rft.date=2016-05-01&rft.volume=49&rft.issue=3&rft.spage=383&rft.epage=391&rft.pages=383-391&rft.issn=0022-0736&rft.eissn=1532-8430&rft_id=info:doi/10.1016/j.jelectrocard.2016.03.017&rft_dat=%3Cproquest_hal_p%3E1786525363%3C/proquest_hal_p%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1786525363&rft_id=info:pmid/27046100&rft_els_id=1_s2_0_S0022073616000984&rfr_iscdi=true |