An automatic pulmonary nodules detection algorithm with multi-scale information fusion_Journal of Biomedical Engineering

Authors：

LIU Xiuling ^1,2 , QI Shuaishuai ¹ , XIONG Peng ^1,2 , LIU Jing ³ , WANG Hongrui ^1,2 ,  YANG Jianli ^1,2

1. College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China;
2. Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P.R.China;
3. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024, P.R.China;

Corresponding author：

YANG Jianli, Email: yangjianli_1987@126.com

Keywords：

multi scale; pulmonary nodule detection; computed tomography images; feature fusion

DOI：

10.7507/1001-5515.201910047

Video：

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Abstract Full text Figures/Tables Video References Cited by

Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.

Citation： LIU Xiuling, QI Shuaishuai, XIONG Peng, LIU Jing, WANG Hongrui, YANG Jianli. An automatic pulmonary nodules detection algorithm with multi-scale information fusion. Journal of Biomedical Engineering, 2020, 37(3): 434-441. doi: 10.7507/1001-5515.201910047 Copy

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1. 陈金东. 中国各类癌症的发病率和死亡率现状及发展趋势. 遵义医学院学报, 2018, 41(6): 5-14.
2. 陈首英, 刘福林, 庞志刚, 等. 肺癌病人5年生存率及生存因素分析. 预防医学论坛, 2004, 10(1): 1-3.
3. Jacobs C, van Rikxoort E M, Twellmann T, et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Analysis, 2014, 18(2): 374-384.
4. Santos A M, Filho A O D C, Silva A C, et al. Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM. Engineering Applications of Artificial Intelligence, 2014, 36(3): 27-39.
5. Fan Weikang, Jiang Huiqin, Ma Ling, et al. A modified faster R-CNN method to improve the performance of the pulmonary nodule detection//Tenth International Conference on Digital Image Processing (ICDIP 2018), Shanghai, 2018. DOI: 10.1117/12.2502893.
6. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition//International Conference on Learning Representations 2015 (ICLR 2015). San Diego, 2015. arXiv: 1409.1556.
7. 葛治文. 基于深度学习的CT图像肺结节检测. 南京: 东南大学, 2018.
8. Pezeshk A, Hamidian S, Petrick N, et al. 3D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE Journal of Biomedical & Health Informatics, 2019, 23(5): 2080-2090.
9. Xie hongtao, Yang Dongbao, Sun Nannan, et al. Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognition, 2019, 85: 109-119.
10. Neubeck A, Gool L V. Efficient non-maximum suppression//18th International Conference on Pattern Recognition (ICPR'06), Hong Kong: IEEE, 2006: 850-855.
11. Armato S G, Roberts R Y, McNitt-Gray M F, et al. The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI)[DB]. (2000-04-10)[2019-10-25]. https:// wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI.
12. Zang S F, Wen L Y, Bian X, et al. Single-shot refinement neural network for object detection//Conference on Computer Vision and Pattern Recognition, Salt Lake City: IEEE, 2018: 4203-4212.
13. Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 39(6): 1137-1149.
14. Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection. Computer Vision and Pattern Recognition, 2017, 45(3): 2117-2125.

Journal of Biomedical Engineering

An automatic pulmonary nodules detection algorithm with multi-scale information fusion

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