Automated PET-guided liver segmentation from low-contrast CT volumes using probabilistic atlas

Authors:
Changyang Li;Xiuying Wang;Yong Xia;Stefan Eberl;Yong Yin;David Dagan Feng
Affiliations:
Biomedical & Multimedia Information Technology Research Group, University of Sydney, Sydney, NSW 2006, Australia;Biomedical & Multimedia Information Technology Research Group, University of Sydney, Sydney, NSW 2006, Australia;Biomedical & Multimedia Information Technology Research Group, University of Sydney, Sydney, NSW 2006, Australia and Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, N ...;Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia;Department of Radiation Oncology, Shandong Tumor Hospital, Jinan, China;Biomedical & Multimedia Information Technology Research Group, University of Sydney, Sydney, NSW 2006, Australia and Center for Multimedia Signal Processing (CMSP), Department of Electronic & Info ...
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

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Abstract

The use of the functional PET information from PET-CT scans to improve liver segmentation from low-contrast CT data is yet to be fully explored. In this paper, we fully utilize PET information to tackle challenging liver segmentation issues including (1) the separation and removal of the surrounding muscles from liver region of interest (ROI), (2) better localization and mapping of the probabilistic atlas onto the low-contrast CT for a more accurate tissue classification, and (3) an improved initial estimation of the liver ROI to speed up the convergence of the expectation-maximization (EM) algorithm for the Gaussian distribution mixture model under the guidance of a probabilistic atlas. The primary liver extraction from the PET volume provides a simple mechanism to avoid the complicated pre-processing of feature extraction as used in the existing liver CT segmentation methods. It is able to guide the probabilistic atlas to better conform to the CT liver region and hence helps to overcome the challenge posed by liver shape variability. Our proposed method was evaluated against manual segmentation by experienced radiologists. Experimental results on 35 clinical PET-CT studies demonstrated that our method is accurate and robust in automated normal liver segmentation.