Liver cancer is a common type of malignant tumor in digestive system. At present, computed tomography (CT) plays an important role in the diagnosis and treatment of liver cancer. Segmentation of tumor lesions based on CT is thus critical in clinical diagnosis and treatment. Due to the limitations of manual segmentation, such as inefficiency and subjectivity, the automatic and accurate segmentation based on advanced computational techniques is becoming more and more popular. In this review, we summarize the research progress of automatic segmentation of liver cancer lesions based on CT scans. By comparing and analyzing the results of experiments, this review evaluate various methods objectively, so that researchers in related fields can better understand the current research progress of liver cancer segmentation based on CT scans.
肝癌是一种常见的消化系统恶性肿瘤。目前电子计算机断层扫描成像（CT）技术已在肝癌诊疗方面发挥着重要作用，而基于 CT 图像的肝癌病灶分割也在临床诊疗中扮演着重要角色。由于人工分割可能存在效率低、主观性强等缺点，因此利用电子计算机来实现对 CT 图像中肝癌病灶的准确、自动分割是当前的研究热点。本文就基于 CT 图像的肝癌病灶自动分割的进展予以综述，通过对比分析实验结果，评估各种分割方法，以便相关领域的科研工作者更好地了解目前肝癌 CT 分割方法的研究进展。.
Keywords: automatic segmentation; computed tomography; liver cancer; machine learning.