Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method.

CT的肝分割被认为是计算机辅助临床应用的前提。然而，由于形状可变和对比度低，自动肝分割技术仍然面临挑战。本文提出了一种基于患者特定概率图谱（PA）的方法，该方法结合了改进的距离正则化水平集进行肝脏分割。首先，计算训练图集和测试患者图像之间的相似度，得出一系列加权图集，用于生成患者特定的PA。然后，可以根据患者特定的PA确定最可能的肝脏区域（MLLR）。最后，通过修改后的距离正则化水平集模型进行细化，该模型利用边缘和区域信息作为气球力。