1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China
2. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
3. Department of Computer Science, Stony Brook University, Stony Brook 11790, USA
Corresponding author (email@example.com
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.
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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