Abstract
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.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant 61432012 and U1435213 and by the Science and Technology Project of Chengdu under Grant 2017-CY02-00030-GX.
Funding
This study was funded by the National Natural Science Foundation of China under Grant 61432012 and U1435213 and by the Science and Technology Project of Chengdu under Grant 2017-CY02-00030-GX.
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Chen, S., Guo, J., Wang, C. et al. DeepLNAnno: a Web-Based Lung Nodules Annotating System for CT Images. J Med Syst 43, 197 (2019). https://doi.org/10.1007/s10916-019-1258-9
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DOI: https://doi.org/10.1007/s10916-019-1258-9