DeepLNAnno: a Web-Based Lung Nodules Annotating System for CT Images

被引：13

作者：

Chen, Sihang ^{[1
]}

Guo, Jixiang ^{[1
]}

Wang, Chengdi ^{[2
]}

Xu, Xiuyuan ^{[1
]}

Yi, Zhang ^{[1
]}

Li, Weimin ^{[2
]}

机构：

[1] Sichuan Univ, Coll Comp Sci, Machine Intelligence Lab, Chengdu 610065, Sichuan, Peoples R China

[2] Sichuan Univ, West China Hosp, West China Sch, Dept Resp & Crit Care Med, Chengdu 610041, Sichuan, Peoples R China

来源：

JOURNAL OF MEDICAL SYSTEMS | 2019年 / 43卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Annotating system; Neural network; Medical application; Medical data collection; AUTOMATIC DETECTION; PULMONARY NODULES;

D O I：

10.1007/s10916-019-1258-9

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Lung cancer is one of the most common and fatal types of cancer, and pulmonary nodule detection plays a crucial role in the screening and diagnosis of this disease. A well-trained deep neural network model can help doctors to find nodules on computed tomography(CT) images while requiring lots of labeled data. However, currently available annotating systems are not suitable for annotating pulmonary nodules in CT images. We propose a web-based lung nodules annotating system named as DeepLNAnno. DeepLNAnno has a unique three-tier working process and loads of features like semi-automatic annotation, which not only make it much easier for doctors to annotate compared to some other annotating systems but also increase the accuracy of the labels. We invited a medical group from West China Hospital to annotate the CT images using our DeepLNAnno system, and collected a large number of labeled data. The results of our experiments demonstrated that a usable nodule-detection system is developed, and good benchmark scores on our evaluation data are obtained.

引用

页数：9

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