Literature DB >> 27349420

Volumetric analysis of pelvic hematomas after blunt trauma using semi-automated seeded region growing segmentation: a method validation study.

David Dreizin1, Uttam K Bodanapally2, Nagaraj Neerchal3, Nikki Tirada4, Michael Patlas5, Edward Herskovits2.   

Abstract

OBJECTIVE: Manually segmented traumatic pelvic hematoma volumes are strongly predictive of active bleeding at conventional angiography, but the method is time intensive, limiting its clinical applicability. We compared volumetric analysis using semi-automated region growing segmentation to manual segmentation and diameter-based size estimates in patients with pelvic hematomas after blunt pelvic trauma.
MATERIALS AND METHODS: A 14-patient cohort was selected in an anonymous randomized fashion from a dataset of patients with pelvic binders at MDCT, collected retrospectively as part of a HIPAA-compliant IRB-approved study from January 2008 to December 2013. To evaluate intermethod differences, one reader (R1) performed three volume measurements using the manual technique and three volume measurements using the semi-automated technique. To evaluate interobserver differences for semi-automated segmentation, a second reader (R2) performed three semi-automated measurements. One-way analysis of variance was used to compare differences in mean volumes. Time effort was also compared. Correlation between the two methods as well as two shorthand appraisals (greatest diameter, and the ABC/2 method for estimating ellipsoid volumes) was assessed with Spearman's rho (r).
RESULTS: Intraobserver variability was lower for semi-automated compared to manual segmentation, with standard deviations ranging between ±5-32 mL and ±17-84 mL, respectively (p = 0.0003). There was no significant difference in mean volumes between the two readers' semi-automated measurements (p = 0.83); however, means were lower for the semi-automated compared with the manual technique (manual: mean and SD 309.6 ± 139 mL; R1 semi-auto: 229.6 ± 88.2 mL, p = 0.004; R2 semi-auto: 243.79 ± 99.7 mL, p = 0.021). Despite differences in means, the correlation between the two methods was very strong and highly significant (r = 0.91, p < 0.001). Correlations with diameter-based methods were only moderate and nonsignificant. Mean semi-automated segmentation time effort was 2 min and 6 s and 2 min and 35 s for R1 and R2, respectively, vs. 22 min and 8 s for manual segmentation.
CONCLUSION: Semi-automated pelvic hematoma volumes correlate strongly with manually segmented volumes. Since semi-automated segmentation can be performed reliably and efficiently, volumetric analysis of traumatic pelvic hematomas is potentially valuable at the point-of-care.

Entities:  

Keywords:  Bleeding pelvic fractures; Hematoma volume; Manual segmentation; Pelvic trauma; Seeded region growing segmentation; Semi-automated volumetric analysis

Mesh:

Substances:

Year:  2016        PMID: 27349420     DOI: 10.1007/s00261-016-0822-8

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  9 in total

1.  Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

Authors:  David Dreizin; Yuyin Zhou; Yixiao Zhang; Nikki Tirada; Alan L Yuille
Journal:  J Digit Imaging       Date:  2020-02       Impact factor: 4.056

2.  Commentary on "Multidetector CT in Vascular Injuries Resulting from Pelvic Fractures".

Authors:  David Dreizin
Journal:  Radiographics       Date:  2019 Nov-Dec       Impact factor: 5.333

3.  Quantitative MDCT assessment of binder effects after pelvic ring disruptions using segmented pelvic haematoma volumes and multiplanar caliper measurements.

Authors:  David Dreizin; Uttam Bodanapally; Daniel Mascarenhas; Robert V O'Toole; Nikki Tirada; Ghada Issa; Jason Nascone
Journal:  Eur Radiol       Date:  2018-03-13       Impact factor: 5.315

4.  A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation.

Authors:  David Dreizin; Yuyin Zhou; Shuhao Fu; Yan Wang; Guang Li; Kathryn Champ; Eliot Siegel; Ze Wang; Tina Chen; Alan L Yuille
Journal:  Radiol Artif Intell       Date:  2020-11-11

5.  Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

Authors:  David Dreizin; Yuyin Zhou; Tina Chen; Guang Li; Alan L Yuille; Ashley McLenithan; Jonathan J Morrison
Journal:  J Trauma Acute Care Surg       Date:  2020-03       Impact factor: 3.697

6.  Blunt splenic injury: Assessment of follow-up CT utility using quantitative volumetry.

Authors:  David Dreizin; Theresa Yu; Kaitlynn Motley; Guang Li; Jonathan J Morrison; Yuanyuan Liang
Journal:  Front Radiol       Date:  2022-07-22

7.  Volumetric Markers of Body Composition May Improve Personalized Prediction of Major Arterial Bleeding After Pelvic Fracture: A Secondary Analysis of the Baltimore CT Prediction Model Cohort.

Authors:  David Dreizin; Remberto Rosales; Guang Li; Hassan Syed; Rong Chen
Journal:  Can Assoc Radiol J       Date:  2020-09-10       Impact factor: 2.248

8.  An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT.

Authors:  David Dreizin; Florian Goldmann; Christina LeBedis; Alexis Boscak; Matthew Dattwyler; Uttam Bodanapally; Guang Li; Stephan Anderson; Andreas Maier; Mathias Unberath
Journal:  J Digit Imaging       Date:  2021-01-21       Impact factor: 4.056

9.  The size of pelvic hematoma can be a predictive factor for angioembolization in hemodynamically unstable pelvic trauma.

Authors:  Hak-Jae Lee; Hyo-Keun No; Nak-Joon Choi; Hyun-Woo Sun; Jae-Suk Lee; Yoon-Joong Jung; Suk-Kyung Hong
Journal:  Ann Surg Treat Res       Date:  2020-02-28       Impact factor: 1.859

  9 in total

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