Constructing a probabilistic model for automated liver region segmentation using non-contrast x-ray torso CT images

Authors:
Xiangrong Zhou;Teruhiko Kitagawa;Takeshi Hara;Hiroshi Fujita;Xuejun Zhang;Ryujiro Yokoyama;Hiroshi Kondo;Masayuki Kanematsu;Hiroaki Hoshi
Affiliations:
Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu, Japan;Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu, Japan;Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu, Japan;Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu, Japan;Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu, Japan;Department of Radiology, Gifu University School of Medicine and University Hospital, Gifu, Japan;Department of Radiology, Gifu University School of Medicine and University Hospital, Gifu, Japan;Department of Radiology, Gifu University School of Medicine and University Hospital, Gifu, Japan;Department of Radiology, Gifu University School of Medicine and University Hospital, Gifu, Japan
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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Abstract

A probabilistic model was proposed in this research for fully-automated segmentation of liver region in non-contrast X-ray torso CT images. This probabilistic model was composed of two kinds of probability that show the location and density (CT number) of the liver in CT images. The probability of the liver on the spatial location was constructed from a number of CT scans in which the liver regions were pre-segmented manually as gold standards. The probability of the liver on density was estimated specifically using a Gaussian function. The proposed probabilistic model was used for automated liver segmentation from non-contrast CT images. 132 cases of the CT scans were used for the probabilistic model construction and then this model was applied to segment liver region based on a leave-one-out method. The performances of the probabilistic model were evaluated by comparing the segmented liver with the gold standard in each CT case. The validity and usefulness of the proposed model were proved.