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A CADe system for nodule detection in thoracic CT images based on artificial neural network

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Abstract

Lung cancer has been the leading cause of cancer-related deaths in 2015 in United States. Early detection of lung nodules will undoubtedly increase the five-year survival rate for lung cancer according to prior studies. In this paper, we propose a novel rating method based on geometrical and statistical features to extract initial nodule candidates and an artificial neural network approach to the detection of lung nodules. The novel method is solely based on 3D distribution of neighboring voxels instead of user-specified features. During initial candidates detection, we combine organized region properties calculated from connected component analysis with corresponding voxel value distributions from statistical analysis to reduce false positives while retaining true nodules. Then we devise multiple artificial neural networks (ANNs) trained from massive voxel neighbor sampling of different types of nodules and organize the outputs using a 3D scoring method to identify final nodules. The experiments on 107 CT cases with 252 nodules in LIDC-IDRI data sets have shown that our new method achieves sensitivity of 89.4% while reducing the false positives to 2.0 per case. Our comprehensive experiments have demonstrated our system would be of great assistance for diagnosis of lung nodules in clinical treatments.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61190120, 61190121, 61190125, 61532002, 61300068, 61300067, 61672149, 61672077), National Science Foundation of USA (Grant Nos. IIS-0949467, IIS-1047715, IIS-1049448), Postdoctoral Science Foundation of China (Grant No. 2013M530512), and China Scholarship Council (Grant No. 201506020035).

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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    Xinglong Liu & Aimin Hao

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Fei Hou

  3. Department of Computer Science, Stony Brook University, Stony Brook, 11790, USA

    Hong Qin

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  1. Xinglong Liu
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  2. Fei Hou
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  3. Hong Qin
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  4. Aimin Hao
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Correspondence to Fei Hou.

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Supporting information The supporting information is available online at info.scichina.com and link. springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Liu, X., Hou, F., Qin, H. et al. A CADe system for nodule detection in thoracic CT images based on artificial neural network. Sci. China Inf. Sci. 60, 072106 (2017). https://doi.org/10.1007/s11432-016-9008-0

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