Way Cherng Chen1, Ying Min Wang1, Kai-hsiang Chuang1, Xavier Golay2, Anqi Qiu3
1Singapore Bioimaging Consortium, A*STAR, Singapore, Singapore; 2University College London, UK; 3National University of Singapore, Singapore
An automatic segmentation approach that integrates both a probabilistic model and an atlas-based method for utilizing both image intensity and liver shape information was developed for fast and accurate liver segmentation. An average volume difference of 20% and overlap ratio of 70.3% between the automatic segmentation and manual segmentation was obtained from 5 mice images. These variabilities are due to the limited contrast between the liver and surrounding tissues. More subjects are needed to test the robustness of the algorithm. The segmentation accuracy should be further improved by using a probabilistic atlas based on a large number of subjects.