Improving CCTA‐based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation
Purpose The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm that is used to determine the hemodynamic significance of a coronary artery stenosis from coronary computed tomography angiogr...
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| Veröffentlicht in: | Medical physics (Lancaster) 2017-03, Vol.44 (3), p.1040-1049 |
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| creator | Freiman, Moti Nickisch, Hannes Prevrhal, Sven Schmitt, Holger Vembar, Mani Maurovich‐Horvat, Pál Donnelly, Patrick Goshen, Liran |
| description | Purpose
The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm that is used to determine the hemodynamic significance of a coronary artery stenosis from coronary computed tomography angiography (CCTA).
Materials and methods
Two sets of data were used in our work: (a) multivendor CCTA datasets of 18 subjects from the MICCAI 2012 challenge with automatically generated centerlines and 3 reference segmentations of 78 coronary segments and (b) additional CCTA datasets of 97 subjects with 132 coronary lesions that had invasive reference standard FFR measurements. We extracted the coronary artery centerlines for the 97 datasets by an automated software program followed by manual correction if required. An automatic machine‐learning‐based algorithm segmented the coronary tree with and without accounting for the PVE. We obtained CCTA‐based FFR measurements using a flow simulation in the coronary trees that were generated by the automatic algorithm with and without accounting for PVE. We assessed the potential added value of PVE integration as a part of the automatic coronary lumen segmentation algorithm by means of segmentation accuracy using the MICCAI 2012 challenge framework and by means of flow simulation overall accuracy, sensitivity, specificity, negative and positive predictive values, and the receiver operated characteristic (ROC) area under the curve. We also evaluated the potential benefit of accounting for PVE in automatic segmentation for flow simulation for lesions that were diagnosed as obstructive based on CCTA which could have indicated a need for an invasive exam and revascularization.
Results
Our segmentation algorithm improves the maximal surface distance error by ~39% compared to previously published method on the 18 datasets from the MICCAI 2012 challenge with comparable Dice and mean surface distance. Results with and without accounting for PVE were comparable. In contrast, integrating PVE analysis into an automatic coronary lumen segmentation algorithm improved the flow simulation specificity from 0.6 to 0.68 with the same sensitivity of 0.83. Also, accounting for PVE improved the area under the ROC curve for detecting hemodynamically significant CAD from 0.76 to 0.8 compared to automatic segmentation without PVE analysis with invasive FFR threshold of 0.8 as the reference standard. Accounting for PVE in flow simulation to support th |
| doi_str_mv | 10.1002/mp.12121 |
| format | Article |
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The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm that is used to determine the hemodynamic significance of a coronary artery stenosis from coronary computed tomography angiography (CCTA).
Materials and methods
Two sets of data were used in our work: (a) multivendor CCTA datasets of 18 subjects from the MICCAI 2012 challenge with automatically generated centerlines and 3 reference segmentations of 78 coronary segments and (b) additional CCTA datasets of 97 subjects with 132 coronary lesions that had invasive reference standard FFR measurements. We extracted the coronary artery centerlines for the 97 datasets by an automated software program followed by manual correction if required. An automatic machine‐learning‐based algorithm segmented the coronary tree with and without accounting for the PVE. We obtained CCTA‐based FFR measurements using a flow simulation in the coronary trees that were generated by the automatic algorithm with and without accounting for PVE. We assessed the potential added value of PVE integration as a part of the automatic coronary lumen segmentation algorithm by means of segmentation accuracy using the MICCAI 2012 challenge framework and by means of flow simulation overall accuracy, sensitivity, specificity, negative and positive predictive values, and the receiver operated characteristic (ROC) area under the curve. We also evaluated the potential benefit of accounting for PVE in automatic segmentation for flow simulation for lesions that were diagnosed as obstructive based on CCTA which could have indicated a need for an invasive exam and revascularization.
Results
Our segmentation algorithm improves the maximal surface distance error by ~39% compared to previously published method on the 18 datasets from the MICCAI 2012 challenge with comparable Dice and mean surface distance. Results with and without accounting for PVE were comparable. In contrast, integrating PVE analysis into an automatic coronary lumen segmentation algorithm improved the flow simulation specificity from 0.6 to 0.68 with the same sensitivity of 0.83. Also, accounting for PVE improved the area under the ROC curve for detecting hemodynamically significant CAD from 0.76 to 0.8 compared to automatic segmentation without PVE analysis with invasive FFR threshold of 0.8 as the reference standard. Accounting for PVE in flow simulation to support the detection of hemodynamic significant disease in CCTA‐based obstructive lesions improved specificity from 0.51 to 0.73 with same sensitivity of 0.83 and the area under the curve from 0.69 to 0.79. The improvement in the AUC was statistically significant (N = 76, Delong's test, P = 0.012).
Conclusion
Accounting for the partial volume effects in automatic coronary lumen segmentation algorithms has the potential to improve the accuracy of CCTA‐based hemodynamic assessment of coronary artery lesions.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1002/mp.12121</identifier><identifier>PMID: 28112409</identifier><language>eng</language><publisher>United States</publisher><subject>Area Under Curve ; Computed Tomography Angiography - methods ; Coronary Angiography - methods ; coronary artery disease ; coronary CT angiography ; Coronary Stenosis - diagnostic imaging ; Coronary Stenosis - physiopathology ; Coronary Vessels - diagnostic imaging ; Coronary Vessels - physiopathology ; Datasets as Topic ; fractional flow reserve simulation ; Hemodynamics ; Humans ; Imaging, Three-Dimensional - methods ; Machine Learning ; Models, Cardiovascular ; partial volume effect ; Pattern Recognition, Automated ; Retrospective Studies ; ROC Curve ; segmentation ; Software</subject><ispartof>Medical physics (Lancaster), 2017-03, Vol.44 (3), p.1040-1049</ispartof><rights>2017 American Association of Physicists in Medicine</rights><rights>2017 American Association of Physicists in Medicine.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3211-69bb4c3dbf30f73e5bfed97d1778fab55de87467110d5274ec79db1f664eabb93</citedby><cites>FETCH-LOGICAL-c3211-69bb4c3dbf30f73e5bfed97d1778fab55de87467110d5274ec79db1f664eabb93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fmp.12121$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fmp.12121$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28112409$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Freiman, Moti</creatorcontrib><creatorcontrib>Nickisch, Hannes</creatorcontrib><creatorcontrib>Prevrhal, Sven</creatorcontrib><creatorcontrib>Schmitt, Holger</creatorcontrib><creatorcontrib>Vembar, Mani</creatorcontrib><creatorcontrib>Maurovich‐Horvat, Pál</creatorcontrib><creatorcontrib>Donnelly, Patrick</creatorcontrib><creatorcontrib>Goshen, Liran</creatorcontrib><title>Improving CCTA‐based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>Purpose
The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm that is used to determine the hemodynamic significance of a coronary artery stenosis from coronary computed tomography angiography (CCTA).
Materials and methods
Two sets of data were used in our work: (a) multivendor CCTA datasets of 18 subjects from the MICCAI 2012 challenge with automatically generated centerlines and 3 reference segmentations of 78 coronary segments and (b) additional CCTA datasets of 97 subjects with 132 coronary lesions that had invasive reference standard FFR measurements. We extracted the coronary artery centerlines for the 97 datasets by an automated software program followed by manual correction if required. An automatic machine‐learning‐based algorithm segmented the coronary tree with and without accounting for the PVE. We obtained CCTA‐based FFR measurements using a flow simulation in the coronary trees that were generated by the automatic algorithm with and without accounting for PVE. We assessed the potential added value of PVE integration as a part of the automatic coronary lumen segmentation algorithm by means of segmentation accuracy using the MICCAI 2012 challenge framework and by means of flow simulation overall accuracy, sensitivity, specificity, negative and positive predictive values, and the receiver operated characteristic (ROC) area under the curve. We also evaluated the potential benefit of accounting for PVE in automatic segmentation for flow simulation for lesions that were diagnosed as obstructive based on CCTA which could have indicated a need for an invasive exam and revascularization.
Results
Our segmentation algorithm improves the maximal surface distance error by ~39% compared to previously published method on the 18 datasets from the MICCAI 2012 challenge with comparable Dice and mean surface distance. Results with and without accounting for PVE were comparable. In contrast, integrating PVE analysis into an automatic coronary lumen segmentation algorithm improved the flow simulation specificity from 0.6 to 0.68 with the same sensitivity of 0.83. Also, accounting for PVE improved the area under the ROC curve for detecting hemodynamically significant CAD from 0.76 to 0.8 compared to automatic segmentation without PVE analysis with invasive FFR threshold of 0.8 as the reference standard. Accounting for PVE in flow simulation to support the detection of hemodynamic significant disease in CCTA‐based obstructive lesions improved specificity from 0.51 to 0.73 with same sensitivity of 0.83 and the area under the curve from 0.69 to 0.79. The improvement in the AUC was statistically significant (N = 76, Delong's test, P = 0.012).
Conclusion
Accounting for the partial volume effects in automatic coronary lumen segmentation algorithms has the potential to improve the accuracy of CCTA‐based hemodynamic assessment of coronary artery lesions.</description><subject>Area Under Curve</subject><subject>Computed Tomography Angiography - methods</subject><subject>Coronary Angiography - methods</subject><subject>coronary artery disease</subject><subject>coronary CT angiography</subject><subject>Coronary Stenosis - diagnostic imaging</subject><subject>Coronary Stenosis - physiopathology</subject><subject>Coronary Vessels - diagnostic imaging</subject><subject>Coronary Vessels - physiopathology</subject><subject>Datasets as Topic</subject><subject>fractional flow reserve simulation</subject><subject>Hemodynamics</subject><subject>Humans</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Machine Learning</subject><subject>Models, Cardiovascular</subject><subject>partial volume effect</subject><subject>Pattern Recognition, Automated</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>segmentation</subject><subject>Software</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kUtuFDEQhi1ERIaAlBNE3sGmE7tfHi-jUQKRgmAR1i0_yoNR2-50dU80uxyBFQfkJLiZBFaoFiXV_-lTqYqQU87OOWPlRRjOeZnrBVmVtaiKumTyJVkxJuuirFlzTF4jfmeMtVXDXpHjcs15nssV-XkThjHtfNzSzebu8tfjD60QLO0BfYr4jn6DkOw-quANRb-N3nmjogGqEAExQJyo3lNlTJrjtHhcGumgxsmrnu5SPwegWQH9kvlI1TyloKasM2lMUY17ujCRImwXW45SfEOOnOoR3j71E_L1-upu87G4_fzhZnN5W5iq5Lxopda1qax2FXOigkY7sFJYLsTaKd00FtaibgXnzDalqMEIaTV3bVuD0lpWJ-T9wZuPcD8DTl3waKDvVYQ0Y8fXLW-k4LL5h5oxIY7gumH0Ia_fcdYtX-jC0P35QkbPnqyzDmD_gs9nz0BxAB58D_v_irpPXw7C33dZlUc</recordid><startdate>201703</startdate><enddate>201703</enddate><creator>Freiman, Moti</creator><creator>Nickisch, Hannes</creator><creator>Prevrhal, Sven</creator><creator>Schmitt, Holger</creator><creator>Vembar, Mani</creator><creator>Maurovich‐Horvat, Pál</creator><creator>Donnelly, Patrick</creator><creator>Goshen, Liran</creator><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></search><sort><creationdate>201703</creationdate><title>Improving CCTA‐based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation</title><author>Freiman, Moti ; Nickisch, Hannes ; Prevrhal, Sven ; Schmitt, Holger ; Vembar, Mani ; Maurovich‐Horvat, Pál ; Donnelly, Patrick ; Goshen, Liran</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3211-69bb4c3dbf30f73e5bfed97d1778fab55de87467110d5274ec79db1f664eabb93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Area Under Curve</topic><topic>Computed Tomography Angiography - methods</topic><topic>Coronary Angiography - methods</topic><topic>coronary artery disease</topic><topic>coronary CT angiography</topic><topic>Coronary Stenosis - diagnostic imaging</topic><topic>Coronary Stenosis - physiopathology</topic><topic>Coronary Vessels - diagnostic imaging</topic><topic>Coronary Vessels - physiopathology</topic><topic>Datasets as Topic</topic><topic>fractional flow reserve simulation</topic><topic>Hemodynamics</topic><topic>Humans</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Machine Learning</topic><topic>Models, Cardiovascular</topic><topic>partial volume effect</topic><topic>Pattern Recognition, Automated</topic><topic>Retrospective Studies</topic><topic>ROC Curve</topic><topic>segmentation</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Freiman, Moti</creatorcontrib><creatorcontrib>Nickisch, Hannes</creatorcontrib><creatorcontrib>Prevrhal, Sven</creatorcontrib><creatorcontrib>Schmitt, Holger</creatorcontrib><creatorcontrib>Vembar, Mani</creatorcontrib><creatorcontrib>Maurovich‐Horvat, Pál</creatorcontrib><creatorcontrib>Donnelly, Patrick</creatorcontrib><creatorcontrib>Goshen, Liran</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><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Freiman, Moti</au><au>Nickisch, Hannes</au><au>Prevrhal, Sven</au><au>Schmitt, Holger</au><au>Vembar, Mani</au><au>Maurovich‐Horvat, Pál</au><au>Donnelly, Patrick</au><au>Goshen, Liran</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving CCTA‐based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2017-03</date><risdate>2017</risdate><volume>44</volume><issue>3</issue><spage>1040</spage><epage>1049</epage><pages>1040-1049</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><abstract>Purpose
The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm that is used to determine the hemodynamic significance of a coronary artery stenosis from coronary computed tomography angiography (CCTA).
Materials and methods
Two sets of data were used in our work: (a) multivendor CCTA datasets of 18 subjects from the MICCAI 2012 challenge with automatically generated centerlines and 3 reference segmentations of 78 coronary segments and (b) additional CCTA datasets of 97 subjects with 132 coronary lesions that had invasive reference standard FFR measurements. We extracted the coronary artery centerlines for the 97 datasets by an automated software program followed by manual correction if required. An automatic machine‐learning‐based algorithm segmented the coronary tree with and without accounting for the PVE. We obtained CCTA‐based FFR measurements using a flow simulation in the coronary trees that were generated by the automatic algorithm with and without accounting for PVE. We assessed the potential added value of PVE integration as a part of the automatic coronary lumen segmentation algorithm by means of segmentation accuracy using the MICCAI 2012 challenge framework and by means of flow simulation overall accuracy, sensitivity, specificity, negative and positive predictive values, and the receiver operated characteristic (ROC) area under the curve. We also evaluated the potential benefit of accounting for PVE in automatic segmentation for flow simulation for lesions that were diagnosed as obstructive based on CCTA which could have indicated a need for an invasive exam and revascularization.
Results
Our segmentation algorithm improves the maximal surface distance error by ~39% compared to previously published method on the 18 datasets from the MICCAI 2012 challenge with comparable Dice and mean surface distance. Results with and without accounting for PVE were comparable. In contrast, integrating PVE analysis into an automatic coronary lumen segmentation algorithm improved the flow simulation specificity from 0.6 to 0.68 with the same sensitivity of 0.83. Also, accounting for PVE improved the area under the ROC curve for detecting hemodynamically significant CAD from 0.76 to 0.8 compared to automatic segmentation without PVE analysis with invasive FFR threshold of 0.8 as the reference standard. Accounting for PVE in flow simulation to support the detection of hemodynamic significant disease in CCTA‐based obstructive lesions improved specificity from 0.51 to 0.73 with same sensitivity of 0.83 and the area under the curve from 0.69 to 0.79. The improvement in the AUC was statistically significant (N = 76, Delong's test, P = 0.012).
Conclusion
Accounting for the partial volume effects in automatic coronary lumen segmentation algorithms has the potential to improve the accuracy of CCTA‐based hemodynamic assessment of coronary artery lesions.</abstract><cop>United States</cop><pmid>28112409</pmid><doi>10.1002/mp.12121</doi><tpages>10</tpages></addata></record> |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete; Alma/SFX Local Collection |
| subjects | Area Under Curve Computed Tomography Angiography - methods Coronary Angiography - methods coronary artery disease coronary CT angiography Coronary Stenosis - diagnostic imaging Coronary Stenosis - physiopathology Coronary Vessels - diagnostic imaging Coronary Vessels - physiopathology Datasets as Topic fractional flow reserve simulation Hemodynamics Humans Imaging, Three-Dimensional - methods Machine Learning Models, Cardiovascular partial volume effect Pattern Recognition, Automated Retrospective Studies ROC Curve segmentation Software |
| title | Improving CCTA‐based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation |
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