CT-based estimation of intracavitary gas volumes using threshold-based segmentation: In vitro study to determine the optimal threshold range
Introduction: This study investigated the optimal Hounsfield unit (HU) threshold range when using threshold‐based segmentation to estimate volumes of contained gas (i.e. intestinal gas) on CT. Methods: A water‐filled cylindrical acrylic imaging phantom containing two saline bags modified to allow in...
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creator | McWilliams, Sebastian Robert O'Connor, Owen J McGarrigle, Anne Marie O'Neill, Siobhan B Quigley, Eamonn MM Shanahan, Fergus Maher, Michael M |
description | Introduction: This study investigated the optimal Hounsfield unit (HU) threshold range when using threshold‐based segmentation to estimate volumes of contained gas (i.e. intestinal gas) on CT.
Methods: A water‐filled cylindrical acrylic imaging phantom containing two saline bags modified to allow injection of known volumes of gas (room air) was constructed. The phantom was imaged with CT following injection of known gas volumes. Images were analysed using standard threshold‐based 3D region growing with human‐entered seed points. The lower threshold was −1024 HU, and upper thresholds between −700 HU and −200 HU were tested for each volume. Appropriate statistical analysis was performed.
Results: Measurements were normally distributed. There was excellent correlation between measured and injected volumes for all thresholds (Pearson's r > 0.99). The optimal upper threshold for small gas volumes (1–6 mL) was −550 HU with 0.1% ± 3.9% (mean ± standard deviation) error. The optimal upper threshold for large gas volumes (10–50 mL) was −350 HU with 0.7 ± 3.6% (mean ± standard deviation) error with Pearson correlations of r > 0.99 for both.
Conclusion: Accurate estimation of gas volumes on CT is possible using threshold‐based segmentation software with a wide range of upper thresholds. The optimal upper threshold for estimation of small volumes (1–6 mL) was −550 HU and −350 HU for volumes of 10–50 mL. |
doi_str_mv | 10.1111/j.1754-9485.2012.02375.x |
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Methods: A water‐filled cylindrical acrylic imaging phantom containing two saline bags modified to allow injection of known volumes of gas (room air) was constructed. The phantom was imaged with CT following injection of known gas volumes. Images were analysed using standard threshold‐based 3D region growing with human‐entered seed points. The lower threshold was −1024 HU, and upper thresholds between −700 HU and −200 HU were tested for each volume. Appropriate statistical analysis was performed.
Results: Measurements were normally distributed. There was excellent correlation between measured and injected volumes for all thresholds (Pearson's r > 0.99). The optimal upper threshold for small gas volumes (1–6 mL) was −550 HU with 0.1% ± 3.9% (mean ± standard deviation) error. The optimal upper threshold for large gas volumes (10–50 mL) was −350 HU with 0.7 ± 3.6% (mean ± standard deviation) error with Pearson correlations of r > 0.99 for both.
Conclusion: Accurate estimation of gas volumes on CT is possible using threshold‐based segmentation software with a wide range of upper thresholds. The optimal upper threshold for estimation of small volumes (1–6 mL) was −550 HU and −350 HU for volumes of 10–50 mL.</description><identifier>ISSN: 1754-9477</identifier><identifier>EISSN: 1754-9485</identifier><identifier>DOI: 10.1111/j.1754-9485.2012.02375.x</identifier><identifier>PMID: 22697325</identifier><language>eng</language><publisher>Melbourne, Australia: Blackwell Publishing Asia</publisher><subject>Algorithms ; computed tomography ; gas volumes ; Gases - analysis ; Humans ; Imaging, Three-Dimensional - methods ; Lung Volume Measurements - methods ; phantom ; Phantoms, Imaging ; region growing ; Reproducibility of Results ; Sensitivity and Specificity ; Solitary Pulmonary Nodule - diagnostic imaging ; threshold-based segmentation ; Tomography, X-Ray Computed - instrumentation ; Tomography, X-Ray Computed - methods</subject><ispartof>Journal of medical imaging and radiation oncology, 2012-06, Vol.56 (3), p.289-294</ispartof><rights>2012 The Authors. Journal of Medical Imaging and Radiation Oncology © 2012 The Royal Australian and New Zealand College of Radiologists</rights><rights>2012 The Authors. Journal of Medical Imaging and Radiation Oncology © 2012 The Royal Australian and New Zealand College of Radiologists.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4095-f18cd9236628e1f2bdd9ca2ce421e48959bc87a203dd8c866cc4b6631d31c2c43</citedby><cites>FETCH-LOGICAL-c4095-f18cd9236628e1f2bdd9ca2ce421e48959bc87a203dd8c866cc4b6631d31c2c43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fj.1754-9485.2012.02375.x$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fj.1754-9485.2012.02375.x$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,778,782,1414,27907,27908,45557,45558</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22697325$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>McWilliams, Sebastian Robert</creatorcontrib><creatorcontrib>O'Connor, Owen J</creatorcontrib><creatorcontrib>McGarrigle, Anne Marie</creatorcontrib><creatorcontrib>O'Neill, Siobhan B</creatorcontrib><creatorcontrib>Quigley, Eamonn MM</creatorcontrib><creatorcontrib>Shanahan, Fergus</creatorcontrib><creatorcontrib>Maher, Michael M</creatorcontrib><title>CT-based estimation of intracavitary gas volumes using threshold-based segmentation: In vitro study to determine the optimal threshold range</title><title>Journal of medical imaging and radiation oncology</title><addtitle>J Med Imaging Radiat Oncol</addtitle><description>Introduction: This study investigated the optimal Hounsfield unit (HU) threshold range when using threshold‐based segmentation to estimate volumes of contained gas (i.e. intestinal gas) on CT.
Methods: A water‐filled cylindrical acrylic imaging phantom containing two saline bags modified to allow injection of known volumes of gas (room air) was constructed. The phantom was imaged with CT following injection of known gas volumes. Images were analysed using standard threshold‐based 3D region growing with human‐entered seed points. The lower threshold was −1024 HU, and upper thresholds between −700 HU and −200 HU were tested for each volume. Appropriate statistical analysis was performed.
Results: Measurements were normally distributed. There was excellent correlation between measured and injected volumes for all thresholds (Pearson's r > 0.99). The optimal upper threshold for small gas volumes (1–6 mL) was −550 HU with 0.1% ± 3.9% (mean ± standard deviation) error. The optimal upper threshold for large gas volumes (10–50 mL) was −350 HU with 0.7 ± 3.6% (mean ± standard deviation) error with Pearson correlations of r > 0.99 for both.
Conclusion: Accurate estimation of gas volumes on CT is possible using threshold‐based segmentation software with a wide range of upper thresholds. The optimal upper threshold for estimation of small volumes (1–6 mL) was −550 HU and −350 HU for volumes of 10–50 mL.</description><subject>Algorithms</subject><subject>computed tomography</subject><subject>gas volumes</subject><subject>Gases - analysis</subject><subject>Humans</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Lung Volume Measurements - methods</subject><subject>phantom</subject><subject>Phantoms, Imaging</subject><subject>region growing</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Solitary Pulmonary Nodule - diagnostic imaging</subject><subject>threshold-based segmentation</subject><subject>Tomography, X-Ray Computed - instrumentation</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>1754-9477</issn><issn>1754-9485</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkctu1DAUhiNE1Rt9BeQlm6S-JI7DBqGoLYN6kVBRl5Zjn0wzJPFgO2XmHXhonM4wbPHGR_L3_-f4P0mCCM5IPJerjJRFnla5KDKKCc0wZWWRbd4kp4eHt4e6LE-SM-9XGHNC8uo4OaGUVyWjxWnyu35MG-XBIPChG1To7Ihsi7oxOKXVSxeU26Kl8ujF9tMAHk2-G5coPDvwz7Y3e7WH5QBjeNV_RIsRRaWzyIfJbFGwyEAAN3QjRCUgu5579f9ckFPjEt4lR63qPVzs7_Pk-_XVY_0lvX24WdSfb1Od46pIWyK0qSjjnAogLW2MqbSiGnJKIBdVUTValIpiZozQgnOt84ZzRgwjmuqcnScfdr5rZ39O8eNy6LyGvlcj2MlLgikWDPNKRFTsUO2s9w5auXZxdLeNkJx3IVdyjlnOkct5F_J1F3ITpe_3XaZmAHMQ_g0_Ap92wK-uh-1_G8uvd4tvD3MdHdKdQ-cDbA4Oyv2QvJzhp_sbec2e6uKurGXO_gC69arN</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>McWilliams, Sebastian Robert</creator><creator>O'Connor, Owen J</creator><creator>McGarrigle, Anne Marie</creator><creator>O'Neill, Siobhan B</creator><creator>Quigley, Eamonn MM</creator><creator>Shanahan, Fergus</creator><creator>Maher, Michael M</creator><general>Blackwell Publishing Asia</general><scope>BSCLL</scope><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>201206</creationdate><title>CT-based estimation of intracavitary gas volumes using threshold-based segmentation: In vitro study to determine the optimal threshold range</title><author>McWilliams, Sebastian Robert ; O'Connor, Owen J ; McGarrigle, Anne Marie ; O'Neill, Siobhan B ; Quigley, Eamonn MM ; Shanahan, Fergus ; Maher, Michael M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4095-f18cd9236628e1f2bdd9ca2ce421e48959bc87a203dd8c866cc4b6631d31c2c43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>computed tomography</topic><topic>gas volumes</topic><topic>Gases - analysis</topic><topic>Humans</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Lung Volume Measurements - methods</topic><topic>phantom</topic><topic>Phantoms, Imaging</topic><topic>region growing</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Solitary Pulmonary Nodule - diagnostic imaging</topic><topic>threshold-based segmentation</topic><topic>Tomography, X-Ray Computed - instrumentation</topic><topic>Tomography, X-Ray Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McWilliams, Sebastian Robert</creatorcontrib><creatorcontrib>O'Connor, Owen J</creatorcontrib><creatorcontrib>McGarrigle, Anne Marie</creatorcontrib><creatorcontrib>O'Neill, Siobhan B</creatorcontrib><creatorcontrib>Quigley, Eamonn MM</creatorcontrib><creatorcontrib>Shanahan, Fergus</creatorcontrib><creatorcontrib>Maher, Michael M</creatorcontrib><collection>Istex</collection><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>Journal of medical imaging and radiation oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McWilliams, Sebastian Robert</au><au>O'Connor, Owen J</au><au>McGarrigle, Anne Marie</au><au>O'Neill, Siobhan B</au><au>Quigley, Eamonn MM</au><au>Shanahan, Fergus</au><au>Maher, Michael M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CT-based estimation of intracavitary gas volumes using threshold-based segmentation: In vitro study to determine the optimal threshold range</atitle><jtitle>Journal of medical imaging and radiation oncology</jtitle><addtitle>J Med Imaging Radiat Oncol</addtitle><date>2012-06</date><risdate>2012</risdate><volume>56</volume><issue>3</issue><spage>289</spage><epage>294</epage><pages>289-294</pages><issn>1754-9477</issn><eissn>1754-9485</eissn><abstract>Introduction: This study investigated the optimal Hounsfield unit (HU) threshold range when using threshold‐based segmentation to estimate volumes of contained gas (i.e. intestinal gas) on CT.
Methods: A water‐filled cylindrical acrylic imaging phantom containing two saline bags modified to allow injection of known volumes of gas (room air) was constructed. The phantom was imaged with CT following injection of known gas volumes. Images were analysed using standard threshold‐based 3D region growing with human‐entered seed points. The lower threshold was −1024 HU, and upper thresholds between −700 HU and −200 HU were tested for each volume. Appropriate statistical analysis was performed.
Results: Measurements were normally distributed. There was excellent correlation between measured and injected volumes for all thresholds (Pearson's r > 0.99). The optimal upper threshold for small gas volumes (1–6 mL) was −550 HU with 0.1% ± 3.9% (mean ± standard deviation) error. The optimal upper threshold for large gas volumes (10–50 mL) was −350 HU with 0.7 ± 3.6% (mean ± standard deviation) error with Pearson correlations of r > 0.99 for both.
Conclusion: Accurate estimation of gas volumes on CT is possible using threshold‐based segmentation software with a wide range of upper thresholds. The optimal upper threshold for estimation of small volumes (1–6 mL) was −550 HU and −350 HU for volumes of 10–50 mL.</abstract><cop>Melbourne, Australia</cop><pub>Blackwell Publishing Asia</pub><pmid>22697325</pmid><doi>10.1111/j.1754-9485.2012.02375.x</doi><tpages>6</tpages></addata></record> |
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subjects | Algorithms computed tomography gas volumes Gases - analysis Humans Imaging, Three-Dimensional - methods Lung Volume Measurements - methods phantom Phantoms, Imaging region growing Reproducibility of Results Sensitivity and Specificity Solitary Pulmonary Nodule - diagnostic imaging threshold-based segmentation Tomography, X-Ray Computed - instrumentation Tomography, X-Ray Computed - methods |
title | CT-based estimation of intracavitary gas volumes using threshold-based segmentation: In vitro study to determine the optimal threshold range |
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