CT-based estimation of intracavitary gas volumes using threshold-based segmentation: In vitro study to determine the optimal threshold range

Sebastian Robert McWilliams, Owen J. O'Connor, Anne Marie McGarrigle, Siobhan B. O'Neill, Eamonn Martin Quigley, Fergus Shanahan, Michael M. Maher

Research output: Contribution to journalArticle

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)289-294
Number of pages6
JournalJournal of Medical Imaging and Radiation Oncology
Volume56
Issue number3
DOIs
StatePublished - Jun 1 2012

Keywords

  • Computed tomography
  • Gas volumes
  • Phantom
  • Region growing
  • Threshold-based segmentation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Oncology

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