Abstract
Lung cancer is one of the most diagnosable form of cancer worldwide. Recent researches have showed that the diagnoses of pulmonary nodules in Computed Tomography (CT) chest scans based on deep learning have made a significant progress for the medical diagnoses. However, the existence of many false positives or the high costs of processing time make it impossible to apply to clinical practice. Toward this purpose, this paper proposed a new image processing method to improve the performance by exploiting the power of acceleration technologies via OpenCL. We use parallel programming and pipeline models to parallelize the CT image preprocessing, and classify them by 3D CNNs according to the significant differences between nodules and non-nodules in 3D shapes. Extensive experimental results have shown that image processing can be accelerated significantly on GPU. In addition, the experiments on 500 patients indicate that our proposed method improved the performance by \(12.5\%\) and achieved \(97.78\%\) sensitivity rate for segmentation.
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References
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics 2018. CA: Cancer J. Clin. 68(1), 7–30 (2018)
Dey, R., Lu, Z., Hong, Y.: Diagnostic classification of lung nodules using 3D neural networks, 2018. cite arxiv:1803.07192Comment: Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 2018, IEEE
Choi, W.-J., Choi, T.-S.: Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput. Methods Programs Biomed. 113(1), 37–54 (2014)
Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Schölkopf, B., Platt, J.C., Hoffman, T., (eds.) Advances in Neural Information Processing Systems, vol. 19, pp. 801–808. MIT Press (2007)
Chen, G., Lai, C., Huang, M.: Parallel sparse coding for seafloor image analysis (2015)
Raina, R., Madhavan, A., Ng, A.Y.: Large-scale deep unsupervised learning using graphics processors. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 873–880. ACM (2009)
Urata, M., et al.: Computed tomography hounsfield units can predict breast cancer metastasis to axillary lymph nodes. BMC Cancer 14(1), 730 (2014)
Orth, D., Fosbinder BA RT(R), R.A.: Essentials of Radiologic Science. LWW (2011)
Wright, F.W.: Radiology of the Chest and Related Conditions. CRC Press, Boca Raton (2001)
Kazerooni, E.A., Gross, B.H.: The Core Curriculum: Cardiopulmonary Imaging (The Core Curriculum Series). LWW (2003)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Tracyn Bvendt01. LIDC-IDRI, 2012. Accessed 21 March 2012
Acknowledgments
The authors would like to thank the data providers of [14] for the testing data sets. This work was partially supported by the Natural Science Foundation of Zhejiang, China (Grant No.Y15F020113) and the Ningbo eHealth Project (No.2016C11024).
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Chen, G., Zhang, J., Pan, Y., Pang, C. (2018). An Image Processing Method via OpenCL for Identification of Pulmonary Nodules. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_12
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