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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Veröffentlicht in:Journal of medical imaging and radiation oncology 2012-06, Vol.56 (3), p.289-294
Hauptverfasser: McWilliams, Sebastian Robert, O'Connor, Owen J, McGarrigle, Anne Marie, O'Neill, Siobhan B, Quigley, Eamonn MM, Shanahan, Fergus, Maher, Michael M
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container_end_page 294
container_issue 3
container_start_page 289
container_title Journal of medical imaging and radiation oncology
container_volume 56
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.
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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 &gt; 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 &gt; 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. 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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 &gt; 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 &gt; 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 ; 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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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source MEDLINE; Wiley Online Library Journals Frontfile Complete
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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