ABSTRACT
In recent years nodule candidate detection becomes the basis of the automated pulmonary nodule detection system, of which the the upper bound limit performance is determined by the sensitivity of nodule candidates detection. This paper is to improve the nodule candidate detection using deep neural networks. We treat the nodule detection task as pixel-level segmentation problem. Based on the 2D U-NET network. We build a multi-level network to process each CT slice to detect more nodules. Weighted dice loss function is designed to maintain a high sensitivity. More important, different from normaly segmentation problem, it has a heavily unbalanced positive and negative samples. We proposed a training method to make the network converge easily. We further propose an effective non-maximum suppression (NMS) method to remove duplicate nodules. The proposed framework has been validated on LUNA16 dataset. We achieved 94.3% sensitivity score, and had a 1/3 times of false positives less than the official methods of LUNA which is better for false positive reduction task. We provide a deep neural network solution for nodule candidate detection and the experimental result demonstrates the effectiveness of our method. It can also be used for input of the false positive reduction task.
- Aberle, D. R., Adams, A. M., Berg, C. D., Black, W. C., Clapp, J. D., Fagerstrom, R. M., Gareen, I. F., Constantine, G., Marcus, P. M., and Sicks, J. R. D., "Reduced lung-cancer mortality with low-dose computedtomographic screening.," New England Journal of Medicine 365(5), 395 (2011).Google ScholarCross Ref
- Setio, A. A. A., Traverso, A., de Bel, T., Berens, M. S., van den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M. E., Geurts, B., et al., "Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge," Medical Image Analysis (2017).Google Scholar
- Messay, T. and Rogers, H. S. K., "A new computationally efficient cad system for pulmonary nodule detection in ct imagery," Medical Image Analysis 14(3), 390--406 (2010).Google ScholarCross Ref
- Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S. J., Wille, M. M. W., Naqibullah, M., Sanchez, C. I., and van Ginneken, B., "Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks," IEEE transactions on medical imaging 35(5), 1160--1169 (2016).Google Scholar
- Dou, Q., Chen, H., Yu, L., Qin, J., and Heng, P.-A., "Multilevel contextual 3-d cnns for false positive reduction in pulmonary nodule detection," IEEE Transactions on Biomedical Engineering 64(7), 1558--1567 (2017).Google ScholarCross Ref
- Krizhevsky, A., Sutskever, I., and Hinton, G. E., "Imagenet classification with deep convolutional neural networks," in {International Conference on Neural Information Processing Systems}, 1097--1105 (2012). Google ScholarDigital Library
- Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and Lecun, Y., \Overfeat: Integrated recognition, localization and detection using convolutional networks," in {International Conference on Learning Representations (ICLR2014), CBLS, April 2014}, (2014).Google Scholar
- Long, J., Shelhamer, E., and Darrell, T., "Fully convolutional networks for semantic segmentation," IEEE Transactions on Pattern Analysis Machine Intelligence 39(4), 640--651 (2014). Google ScholarDigital Library
- Ren, S., He, K., Girshick, R., and Sun, J., "Faster r-cnn: towards real-time object detection with region proposal networks," in {International Conference on Neural Information Processing Systems}, 91--99 (2015). Google ScholarDigital Library
- Parkhi, O. M., Vedaldi, A., Zisserman, A., et al., "Deep face recognition.," in {BMVC}, 1(3), 6 (2015).Google Scholar
- He, K., Zhang, X., Ren, S., and Sun, J., "Deep residual learning for image recognition," in {Computer Vision and Pattern Recognition}, 770--778 (2016).Google Scholar
- Gu, D., Li, J., Li, X., and Liang, C., "Visualizing the knowledge structure and evolution of big data research in healthcare informatics," International journal of medical informatics 98, 22--32 (2017).Google Scholar
- Murphy, K., Van, G. B., Schilham, A. M., de Hoop, B. J., Gietema, H. A., and Prokop, M., "A large-scale evaluation of automatic pulmonary nodule detection in chest ct using local image features and k-nearest-neighbour classification," Medical Image Analysis 13(5), 757 (2009).Google ScholarCross Ref
- Jacobs, C., van Rikxoort, E. M., Twellmann, T., Scholten, E. T., de Jong, P. A., Kuhnigk, J. M., Oudkerk, M., de Koning, H. J., Prokop, M., and Schaefer-Prokop, C., "Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images," Medical Image Analysis 18(2), 374 (2014).Google ScholarCross Ref
- Setio, A. A. A., Jacobs, C., Gelderblom, J., and Ginneken, B., "Automatic detection of large pulmonary solid nodules in thoracic ct images," Medical Physics 42(10), 5642--5653 (2015).Google ScholarCross Ref
- Tan, M., Deklerck, R., Jansen, B., Bister, M., and Cornelis, J., "A novel computer-aided lung nodule detection system for ct images," Medical Physics 38(10), 5630 (2011).Google ScholarCross Ref
- Lopez Torres, E., Fiorina, E., Pennazio, F., Peroni, C., Saletta, M., Camarlinghi, N., Fantacci, M., and Cerello, P., "Large scale validation of the m5l lung cad on heterogeneous ct datasets," Medical physics 42(4), 1477--1489 (2015).Google ScholarCross Ref
- Ronneberger, O., Fischer, P., and Brox, T., "U-net: Convolutional networks for biomedical image segmentation," in {International Conference on Medical Image Computing and Computer-Assisted Intervention}, 234--241 (2015).Google Scholar
- Hartigan, J. A. and Wong, M. A., "Algorithm as 136: A k-means clustering algorithm," Journal of the Royal Statistical Society 28(1), 100--108 (1979).Google Scholar
- Gonzalez, Rafael, C., Woods, and Richard, E., "Digital image processing," Prentice Hall International 28(4), 484--486 (1992). Google ScholarDigital Library
- Dice, L. R., "Measures of the amount of ecologic association between species," Ecology 26(3), 297--302 (1945).Google ScholarCross Ref
- Srensen, T., "A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons," Biol. Skr. 5, 1--34 (1948).Google Scholar
- Viola, P. and Jones, M., "Robust real-time face detection," International Journal of Computer Vision 57(2), 137--154 (2004). Google ScholarDigital Library
- Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., and Devin, M., "Tensorflow: Large-scale machine learning on heterogeneous distributed systems," (2016).Google Scholar
- Glorot, X. and Bengio, Y., "Understanding the difficulty of training deep feedforward neural networks," Journal of Machine Learning Research 9, 249--256 (2010).Google Scholar
- Canny, J. F., {A computational approach to edge detection}, Morgan Kaufmann Publishers Inc. (1987).Google Scholar
- Armato, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al., "The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans," Medical physics 38(2), 915--931 (2011).Google ScholarCross Ref
Index Terms
- Pulmonary Nodule Detection in CT Images via Deep Neural Network: Nodule Candidate Detection
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