Semiautomated Segmentation of Pleural Effusions in MDCT Datasets

Falck, Christian von; Meier, Simone; Jördens, Steffen; King, Benjamin; Galanski, Michael; Shin, Hoen‐oh

doi:10.1016/j.acra.2010.02.011

Cited by 15 publications

(17 citation statements)

References 18 publications

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“…Our results concur with the findings of previous studies regarding the degree of correlation between pleural effusion and estimated pleural effusion on CT [4,5]. This observation is important: our approach is comparable to the approach of ultrasonography and chest radiography where practicality is also often more important than accuracy [6][7][8][9].…”

Section: Discussionsupporting

confidence: 83%

“…Nevertheless, measurements could be affected by respiration. However, both the observation that the typical crescent-like shape of non-loculated pleural effusions is also present in postmortem CT and the agreement between previous clinical studies and our results suggest that this effect may not significantly affect volume estimation [4,5]. Second, there were no cases with loculated pleural effusions in our population.…”

Section: Discussionsupporting

confidence: 67%

“…However, comparing CT measurements with autopsy findings permitted us to compare radiologic findings with the actual volume of the pleural effusion in every case. This is a clear advantage over clinical studies where the volume of pleural effusion has either to be quantified during thoracentesis [7,8], which may not represent the total fluid volume-especially in large pleural effusions, or using segmentation techniques [5], which represents a calculated volume. Nevertheless, measurements could be affected by respiration.…”

Section: Discussionmentioning

confidence: 99%

“…Emergency radiologists are regularly asked to quantify the volume of such pleural fluid collections on CT. The size of an effusion can significantly impact patient management, especially in critically ill patients [4,5]. Simple rules of thumb to estimate pleural fluid volume on thoracic sonography and chest radiographs have been proposed in the literature [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Quantification of pleural effusion from single area measurements on CT

et al. 2013

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The objective of this study was to determine if area measurements of pleural fluid on computed tomography (CT) reflect the actual pleural fluid volume (PEvol) as measured at autopsy, to establish a formula to estimate the volume of pleural effusion (PEest), and to test the accuracy and observer reliability of PEest.132 human cadavers, with pleural effusion were divided into phase 1 (n=32) and phase 2 (n=100). In phase 1, PEvol was compared to area measurements on axial (axA), sagittal (sagA), and coronal (corA) CT images. Linear regression analysis was used to create a formula to calculate PEest. In phase 2, intra-class correlation (ICC) was used to assess inter-reader reliability and determine the agreement between PEest and PEvol. PEvol correlated to a higher degree to axA (r s mean= 0.738; p<0.001) than to sagA (r s mean=0.679, p<0.001) and corA (r s mean = 0.709; p < 0.001). PEest can be established with the following formula: axA×0.1=PEest. Mean difference between PEest and PEvol was less than 40 mL (ICC=0.837-0.874; p<0.001). Inter-reader reliability was higher between two experienced readers (ICC=0.984-0.987; p<0.001) than between an inexperienced reader and both experienced readers (ICC =0.660-0.698; p<0.001). Pleural effusions may be quantified in a rapid, reliable, and reasonably accurate fashion using single area measurements on CT.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantification of pleural effusion from single area measurements on CT

et al. 2013

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“…The best segmentation method presented so far came from von Falck et al . [14], but it is not (yet) fully automatic. Though they were able to cut segmentation time by three quarters without sacrificing much segmentation quality, that the software requires physician time at all makes it less than ideal as a routine diagnostic tool (though it may be valuable in special cases).…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Measurement of Pleural Effusions on CT

Yao

Bliton

Summers

2013

IEEE Trans. Biomed. Eng.

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Pleural effusion is an important biomarker for the diagnosis of many diseases. We develop an automated method to evaluate pleural effusion on CT scans, the measurement of which is prohibitively time consuming when performed manually. The method is based on parietal and visceral pleura extraction, active contour models, region growing, Bezier surface fitting, and deformable surface modeling. Twelve CT scans with three manual segmentations were used to validate the automatic segmentation method. The method was then applied on 91 additional scans for visual assessment. The segmentation method yielded a correlation coefficient of 0.97 and a Dice coefficient of 0.72 ± 0.13 when compared to a professional manual segmentation. The visual assessment estimated 83% cases with negligible or small segmentation errors, 14% with medium errors, and 3% with large errors.

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Volumetric Medical Imaging

Yao

Burns

2015

Handbook of Digital Imaging

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In this chapter, the basics of volumetric medical imaging are introduced. The medical imaging modalities, or methodologies of visualization, included in this discussion are computed tomography (CT), magnetic resonance imaging (MRI), molecular/nuclear medicine imaging (SPECT/CT, PET/CT, and PET/MRI), and ultrasound. The physical and clinical practice principles of these techniques for anatomic and pathologic visualization are presented, along with illustrative clinical medical examples.

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Semiautomated Segmentation of Pleural Effusions in MDCT Datasets

Cited by 15 publications

References 18 publications

Quantification of pleural effusion from single area measurements on CT

Quantification of pleural effusion from single area measurements on CT

Automatic Segmentation and Measurement of Pleural Effusions on CT

Volumetric Medical Imaging

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