A Structural Saliency-Based Approach for Automatic Intrahepatic Vascular Separation From Contrast-Enhanced Multi-Phase MR Images

Q. Guo, Hong Song, Jingfan Fan, Danni Ai, Jian Yang, Yuanjin Gao
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Abstract

Intrahepatic vascular separation on contrast-enhanced Magnetic Resonance (MR) images is indispensable for the hepatic tumor surgery. This paper presents an unsupervised frame-work based on structural saliency for automatically separating portal vein (PV) and hepatic vein (HV) from contrast-enhanced multi-phase MR images. In our work, we propose a new multi-scale filter based on statistics and shape information in the region of interest, called SSIROI, with which the vascular connectivity and saliency in the 3D hepatic region can be guaranteed. Experiments are conducted on clinical contrast-enhanced MR images, and the results show that our method achieves effective separation of intrahepatic vasculature by extracting the PV and HV from multi-phase images, and our proposed SSIROI filter outperforms state-of-the-art methods.
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基于结构显著性的多相磁共振图像肝内血管自动分离方法
在肝肿瘤手术中,利用磁共振造影技术分离肝内血管是必不可少的。本文提出了一种基于结构显著性的无监督框架,用于从对比增强的多相MR图像中自动分离门静脉(PV)和肝静脉(HV)。在我们的工作中,我们提出了一种新的基于感兴趣区域的统计和形状信息的多尺度滤波器,称为SSIROI,它可以保证三维肝脏区域的血管连通性和显著性。在临床磁共振增强图像上进行了实验,结果表明,我们的方法通过从多相图像中提取PV和HV,实现了肝内血管的有效分离,并且我们提出的SSIROI滤波器优于现有的方法。
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