J Korean Soc Radiol.  2013 Feb;68(2):125-136. 10.3348/jksr.2013.68.2.125.

Feasibility of Commercially Available, Fully Automated Hepatic CT Volumetry for Assessing Both Total and Territorial Liver Volumes in Liver Transplantation

• Shin CI
• Kim SH
• Rhim JH
• Yi NJ
• Suh KS
• Lee JM
• Han JK
• Choi BI

• KMID: 2097990
• DOI: http://doi.org/10.3348/jksr.2013.68.2.125

Abstract

PURPOSE
To assess the feasibility of commercially-available, fully automated hepatic CT volumetry for measuring both total and territorial liver volumes by comparing with interactive manual volumetry and measured ex-vivo liver volume.
MATERIALS AND METHODS
For the assessment of total and territorial liver volume, portal phase CT images of 77 recipients and 107 donors who donated right hemiliver were used. Liver volume was measured using both the fully automated and interactive manual methods with Advanced Liver Analysis(R) software. The quality of the automated segmentation was graded on a 4-point scale. Grading was performed by two radiologists in consensus. For the cases with excellent-to-good quality, the accuracy of automated volumetry was compared with interactive manual volumetry and measured ex-vivo liver volume which was converted from weight using analysis of variance test and Pearson's or Spearman correlation test. Processing time for both automated and interactive manual methods was also compared.
RESULTS
Excellent-to-good quality of automated segmentation for total liver and right hemiliver was achieved in 57.1% (44/77) and 17.8% (19/107), respectively. For both total and right hemiliver volumes, there were no significant differences among automated, manual, and ex-vivo volumes except between automate volume and manual volume of the total liver (p = 0.011). There were good correlations between automate volume and ex-vivo liver volume (gamma = 0.637 for total liver and gamma = 0.767 for right hemiliver). Both correlation coefficients were higher than those with manual method. Fully automated volumetry required significantly less time than interactive manual method (total liver: 48.6 sec vs. 53.2 sec, right hemiliver: 182 sec vs. 244.5 sec).
CONCLUSION
Fully automated hepatic CT volumetry is feasible and time-efficient for total liver volume measurement. However, its usefulness for territorial liver volumetry needs to be improved.

MeSH Terms

Consensus
Humans
Liver
Liver Transplantation
Multidetector Computed Tomography
Organ Size
Pattern Recognition, Automated
Time Factors
Tissue Donors

Figure

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  Fig. 1 Examples showing the fully automated volumetry and interactive manual volumetry methods. A. Fully automated volumetry for total liver. In a 46-year-old man who underwent liver transplantation for hepatitis B related liver cirrhosis, automated volumetry software segmented the entire liver from volumetric CT and coded for the segmented liver. The measured volume was 558.8 mL (right). Note that the difference between actual liver and segmented liver seems to be < 5% on axial CT image (left), therefore, the segmentation quality was graded as excellent in this patient. B. For interactive manual volumetry for the same patient to A, the radiologist drew a free-hand region-of-interest (solid line) on CT image (left) taken at the mid-level of the liver and then the software segmented and coded the liver. If the segmentation quality was not satisfactory, the radiologist was able to modify the results using either an expansion and shrinkage tool. The measured volume was 525.3 mL (right). C. Fully automated volumetry for territorial right hemiliver. In a 19-year-old man who underwent right hemihepatectomy for liver donation, automated volumetry software segmented and divided liver to right and left hepatic lobes from volumetric CT (left). Right and left hepatic lobes were coded with dark and light grey, respectively. Note that right and left lobes were separated by the middle hepatic vein (arrow in left image). In this patient, right lobe volume was measured as 939.9 mL. D. For interactive manual volumetry for the same patient to C, the radiologist drew a free-hand region-of-interest (solid line) on CT image (left) taken at the mid-level of the liver and then the software segmented and coded the liver. If the segmentation quality was not satisfactory, the radiologist was able to adjust the results using either an expansion and shrinkage tool. The measured volume was 874.2 mL (right).

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  Fig. 2 Examples showing excellent quality of segmentation. A. In a 53-year-old man who underwent liver transplantation due to hepatitis B-related liver cirrhosis, serial axial CT images show an excellent quality of segmentation for the entire liver. Note that umbilical segment of left portal vein (arrow) and intrahepatic inferior vena cava (arrowheads) are included in the segmentation. Measured total liver volume by fully automated software was 722 mL. Estimated ex-vivo liver volume was 684 mL. B. In a 42-year-old man who underwent right hemihepatectomy for liver donation, serial axial CT images show an excellent quality of segmentation for the entire liver as well as for both territorial hemilivers. Right hemiliver is coded with dark grey while left hemiliver is with light grey. Both hemilivers are divided by the middle hepatic vein (arrows) and gallbladder (*). Measured right hemiliver volume by fully automated software was 747 mL. Estimated ex-vivo hemiliver volume was 700 mL.

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  Fig. 3 Examples showing suboptimal segmentation by fully automated software for the total liver. A. Ascites is the cause of mis-segmentation. In a 47-year-old man with hepatitis B-related advanced liver cirrhosis, automated software included a large amount of perihepatic ascites (*) as a part of the liver and included it to the segmented liver volume. Note a small compact lipiodol-laden nodule (arrow) in segment VIII of the liver. B. In a 38-year-old man with hepatitis B-related liver cirrhosis, automated software did not include some part of the left hepatic lobe (arrows) due to unusual extension to left upper quadrant. Also a low-attenuating radiofrequency ablation defect (*) in segment VII was not included during the segmentation.

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  Fig. 4 Examples showing suboptimal segmentation by fully automated software for the right hemiliver. A, B. Automated software mis-segmented the liver in a 35-year-old man (A) and in a 33-year-old man (B) who were a liver donor candidate. Note that right and left hemilivers were erroneously divided at the line of umbilical segment of left portal vein (arrow in A) and at the line of right hepatic vein (arrow in B).

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