论著摘要 |【AI-CT】计算机断层扫描图像中早期肺结节检测的辅助诊断系统(双语版))

2018-02-06 11:23:03 admin 4
标签:   人工智能 CT 肺结节 计算机辅助诊断 集成分类器

An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images.

发表日期: 2016.12.28   来源:J Med Syst. 2017 Feb;41(2):30.

作者:

Liu JK1, Jiang HY2, Gao MD2, He CG3 , Wang Y2, Wang P1, Ma H4, Li Y5.

作者介绍:

1. Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, Guangdong, China.

2. Sino-Dutch Biomedical and Information Engineering School, Hunnan Campus, Northeastern University, Shenyang, 110169, Liaoning, China.

3. Software School, North China University of Water Resources and Electric Power, Zhengzhou, 450045, Henan, China.

4. Sino-Dutch Biomedical and Information Engineering School, Hunnan Campus, Northeastern University, Shenyang, 110169, Liaoning, China. mahe@bmie.neu.edu.cn.

5. Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, Guangdong, China. ye.li@siat.ac.cn.

摘要

肺癌仍然是世界上关注度最高的疾病。肺实质中产生肺结节时,表明有潜在的肺癌发生风险。计算机辅助肺结节检测系统是必要的,可以缩短诊断时间,降低病人死亡率。在这项研究中,我们提出了一个新的计算机辅助诊断(CAD)系统检测早期肺结节,这可以帮助放射科医生快速找到疑似结节并做出判断。该系统由四个主要部分组成:肺实质分割,候选结节检测,特征提取(共22个特征)和结节分类。由肺图像数据库联盟(LIDC)创建的公开数据集用于训练和测试。本研究从80例CT扫描中选取6400个切片,共计978个结节,由4名放射科医师标记。通过本文提出的快速分割方法分割出包括888个真实结节和11379个假阳性结节的肺结节。通过一种集成分类器,随机森林(RF),本研究分别获得93.2,92.4,94.8,97.6%的准确性,敏感性,特异性,曲线下面积(AUC)。与支持向量机(SVM)分类器相比,RF可以减少更多的假阳性结节,获得更大的AUC。在此CAD系统的帮助下,放射科医师可及时得到肺结节诊断的重要参考。

Abstact

Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.

关键词:

计算机辅助诊断;集成分类器;LIDC;肺结节检测

Keywords:

Computer aided diagnosis (CAD); Ensemble classifier; LIDC; Pulmonary nodule detection

阅读原文:PMID: 28032305  DOI: 10.1007/s10916-016-0669-0


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