Wang Xiuying, Zheng Chaojie, Li Changyang, Yin Yong, Feng David Dagan
Biomedical and Multimedia Information Technology, Research Group, School of Information Technologies, University of Sydney, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3415-8. doi: 10.1109/IEMBS.2011.6090924.
In this paper, we propose an automated liver segmentation method to overcome the challenging issues of high degree of variations in liver shape / size and similar density distribution shared by the liver and its surrounding structures. To improve the performance of conventional statistical shape model for liver segmentation, in our method, the signed distance function is utilized so that the landmarks correspondence is not required when performing the principle component analysis. We improve the Chan-Vese model to bind the shape energy and local intensity feature to evolve the surface both globally and locally toward the closest shape driven by the PCA. In our experiments, 20 clinical CT studies were used for training and 25 clinical CT studies were used for validation. Our experimental results demonstrate that our method can achieve accurate and robust liver segmentation from both of low-contrast and high-contrast CT images.
在本文中，我们提出了一种自动肝脏分割方法，以克服肝脏形状/大小高度变化以及肝脏与其周围结构共享相似密度分布等具有挑战性的问题。为了提高传统统计形状模型在肝脏分割方面的性能，在我们的方法中，利用了符号距离函数，这样在进行主成分分析时就不需要地标对应。我们改进了Chan-Vese模型，将形状能量和局部强度特征结合起来，使表面在全局和局部朝着由主成分分析驱动的最接近形状演化。在我们的实验中，使用了20个临床CT研究进行训练，25个临床CT研究进行验证。我们的实验结果表明，我们的方法能够从低对比度和高对比度CT图像中实现准确且稳健的肝脏分割。
