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Published online before print July 20, 2006, 10.1148/radiol.2403050850
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Automated Hepatic Volumetry for Living Related Liver Transplantation At Multisection CT1

Yoshiharu Nakayama, MD, Qiang Li, PhD, Shigehiko Katsuragawa, PhD, Ryuji Ikeda, RT, Yasuhiro Hiai, RT, Kazuo Awai, MD, Shinichiro Kusunoki, MD, Yasuyuki Yamashita, MD, Hideaki Okajima, MD, Yukihiro Inomata, MD and Kunio Doi, PhD

1 From the Departments of Diagnostic Radiology (Y.N., K.A., S. Kusunoki, Y.Y.) and Transplantation/Pediatric Surgery (H.O., Y.I.), Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto City, Kumamoto, 860-8556, Japan; Department of Radiology, the Kurt Rossmann Laboratories for Radiologic Image Research, University of Chicago, Chicago, Ill (Q.L., K.D.); Department of Radiological Technology, School of Health Sciences, Kumamoto University, Kumamoto, Japan (S. Katsuragawa); Department of Radiology, Kumamoto University Hospital, Kumamoto, Japan (R.I., Y.H.). Received May 19, 2005; revision requested July 18; revision received September 4; accepted September 23; final version accepted, November 14. Supported in part by USPHS grants CA 62625 and CA 64370. Address correspondence to Y.N. (e-mail: yosiharu156{at}lily.ocn.ne.jp).


Figure 1
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  		Figure 1: Flowchart of the overall scheme of the automated method for determination of liver volume.

 

Figure 2
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  		Figure 2a: Automated segmentation of the liver on transverse CT images. (a) CT scan shows 3D volume of interest ({square}) with 32 x 32-pixel in-section matrix and automatically determined height for estimation of the mean CT number. (b) The initial candidate liver regions. (c) Selection of 3D object with the maximum volume among all objects by using 3D connected-component labeling technique.

 

Figure 2
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  		Figure 2b: Automated segmentation of the liver on transverse CT images. (a) CT scan shows 3D volume of interest ({square}) with 32 x 32-pixel in-section matrix and automatically determined height for estimation of the mean CT number. (b) The initial candidate liver regions. (c) Selection of 3D object with the maximum volume among all objects by using 3D connected-component labeling technique.

 

Figure 2
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  		Figure 2c: Automated segmentation of the liver on transverse CT images. (a) CT scan shows 3D volume of interest ({square}) with 32 x 32-pixel in-section matrix and automatically determined height for estimation of the mean CT number. (b) The initial candidate liver regions. (c) Selection of 3D object with the maximum volume among all objects by using 3D connected-component labeling technique.

 

Figure 3
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  		Figure 3: Scatterplot shows actual liver volume and weight for seven specimens. The regression live (y = 1.06x) was used for converting liver weight to liver volume.

 

Figure 4
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  		Figure 4: Scatterplot shows measured liver volume and liver volume estimated with the manual method. There is close correlation between manual volumetry and measured liver volume (y = 1.01x, r = 0.899, P < .001).

 

Figure 5
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  		Figure 5: Scatterplot shows measured liver volume and liver volume estimated with the automated method. There is good correlation between computerized volumetry and measured liver volume (y = 1.04x, r = 0.792, P < .001).

 

Figure 6
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  		Figure 6: Scatterplot shows the liver volume estimated with manual and automated methods. There is a close correlation between automated and manual volumetry (y = 1.03x, r = 0.883, P < .001).

 




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