Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT
Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT
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DOI：
10.1007/s11548-019-01991-5
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发表时间：
2019-08-01
影响因子：
3
通讯作者：
Mehndiratta, Amit
中科院分区：
文献类型：
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作者：
Nayak, Akash;Kayal, Esha Baidya;Mehndiratta, Amit
目的近年来，世界各地都报道了肝硬化的高死亡率。肝硬化的早期发现可能有助于控制疾病向肝细胞癌（HCC）的进展。缺乏训练有素的CT放射科医生和增加的患者人数延误了诊断和进一步的管理。本研究提出了一种计算机辅助诊断系统，用于检测肝硬化和HCC，这是一种非常有效和节省时间的方法。方法回顾性分析40例健康（n=14）、肝硬化（n=12）和肝硬化合并HCC （n=14）患者的CT增强数据集（n=40, M:F=5:3，年龄=25-55岁）。提出了一种基于改进区域生长分割技术的肝脏三维自动分割方法，并与目前最先进的基于深度学习的分割技术进行了比较。此外，利用logistic回归从分段CT肝体积中计算直方图参数，用于健康和病变（肝硬化和HCC）肝脏的分类。利用支持向量机（SVM）对CT图像进行多阶段分析，提取24个时间特征，用于肝硬化和肝癌的检测。结果与深度学习算法（健康肝脏：82%，肝硬化：78%，HCC: 70%）相比，该方法获得了更好的三维分割，健康肝脏的Dice系数为90%，肝硬化为86%，HCC为81%。标准差和峰度差异有统计学意义(p
PurposeHigh mortality rate due to liver cirrhosis has been reported over the globe in the previous years. Early detection of cirrhosis may help in controlling the disease progression toward hepatocellular carcinoma (HCC). The lack of trained CT radiologists and increased patient population delays the diagnosis and further management. This study proposes a computer-aided diagnosis system for detecting cirrhosis and HCC in a very efficient and less time-consuming approach.MethodsContrast-enhanced CT dataset of 40 patients (n=40; M:F=5:3; age=25-55years) with three groups of subjects: healthy (n=14), cirrhosis (n=12) and cirrhosis with HCC (n=14), were retrospectively analyzed in this study. A novel method for the automatic 3D segmentation of liver using modified region-growing segmentation technique was developed and compared with the state-of-the-art deep learning-based technique. Further, histogram parameters were calculated from segmented CT liver volume for classification between healthy and diseased (cirrhosis and HCC) liver using logistic regression. Multi-phase analysis of CT images was performed to extract 24 temporal features for detecting cirrhosis and HCC liver using support vector machine (SVM).ResultsThe proposed method produced improved 3D segmentation with Dice coefficient 90% for healthy liver, 86% for cirrhosis and 81% for HCC subjects compared to the deep learning algorithm (healthy: 82%; cirrhosis: 78%; HCC: 70%). Standard deviation and kurtosis were found to be statistically different (p