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KMID : 1146720140010010037
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국제컴퓨터가상수술학회 학술지
2014 Volume.1 No. 1 p.37 ~ p.40
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Automatic Liver Segmentation on Abdominal Contrast-enhanced CT Images for the Pre-surgery Planning of Living Donor Liver Transplantation
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Jang Yu-Jin
Hong Helen
Chung Jin-Wook
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Abstract
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Purpose: For living donor liver transplantation, liver segmentation is difficult due to the variability of its shape across patients and similarity of the density of neighbor organs such as heart, stomach, kidney, and spleen. In this paper, we propose an automatic segmentation of the liver using multi-planar anatomy and deformable surface model in portal phase of abdominal contrast-enhanced CT images.
Method: Our method is composed of four main steps. First, the optimal liver volume is extracted by positional information of pelvis and rib and by separating lungs and heart from CT images. Second, anisotropic diffusing filtering and adaptive thresholding are used to segment the initial liver volume. Third, morphological opening and connected component labeling are applied to multiple planes for removing neighbor organs. Finally, deformable surface model and probability summation map are performed to refine a posterior liver surface and missing left robe in previous step.
Results: All experimental datasets were acquired on ten living donors using a SIEMENS CT system. Each image had a matrix size of 512×512 pixels with in-plane resolutions ranging from 0.54 to 0.70 mm. The slice spacing was 2.0 mm and the number of images per scan ranged from 136 to 229. For accuracy evaluation, the average symmetric surface distance (ASD) and the volume overlap error (VE) between automatic segmentation and manual segmentation by two radiologists are calculated. The ASD was 0.26±0.12mm for manual1 versus automatic and 0.24±0.09mm for manual2 versus automatic while that of inter-radiologists was 0.23±0.05mm . The VE was 0.86±0.45 for manual1 versus automatic and 0.73±0.33 for manaual2 versus automatic while that of inter-radiologist was 0.76±0.21 .
Conclusion: Our method can be used for the liver volumetry for the pre-surgery planning of living donor liver transplantation.
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KEYWORD
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Living donor liver transplantation, Abdominal CT image, Liver volumetry, Liver segmentation
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