Abstract
Pulmonary nodule detection using chest CT scan is an essential but challenging step towards the early diagnosis of lung cancer. Although a number of deep learning-based methods have been published in the literature, these methods still suffer from less accuracy. In this paper, we propose a novel pulmonary module detection method, which uses a 3D residual U-Net (3D RU-Net) for nodule candidate detection and a 3D densely connected CNN (3D DC-Net) for false positive reduction. 3D RU-Net contains residual blocks in both contracting and expansive paths, and 3D DC-Net leverages three dense blocks to facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.
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References
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424 (2018)
Baldwin, D.R.: Prediction of risk of lung cancer in populations and in pulmonary nodules: significant progress to drive changes in paradigms. Lung Cancer 89(1), 1–3 (2015)
Ding, J., Li, A., Hu, Z., Wang, L.: Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 559–567. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_64
Hamidian, S., Sahiner, B., Petrick, N., Pezeshk, A.: 3D convolutional neural network for automatic detection of lung nodules in chest CT. In: Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134, p. 1013409. International Society for Optics and Photonics (2017)
Dou, Q., Chen, H., Jin, Y., Lin, H., Qin, J., Heng, P.-A.: Automated pulmonary nodule detection via 3D convnets with online sample filtering and hybrid-loss residual learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 630–638. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_72
Wang, B., Qi, G., Tang, S., Zhang, L., Deng, L., Zhang, Y.: Automated pulmonary nodule detection: high sensitivity with few candidates. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 759–767. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_84
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Setio, A.A.A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1–13 (2017)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Khosravan, N., Bagci, U.: S4ND: single-shot single-scale lung nodule detection. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 794–802. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_88
Cao, H., et al.: Two-stage convolutional neural network architecture for lung nodule detection. arXiv preprint arXiv:1905.03445 (2019)
Acknowledgement
This work was supported in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grants JCYJ20180306171334997, in part by the National Natural Science Foundation of China under Grants 61771397, in part by Synergy Innovation Foundation of the University and Enterprise for Graduate Students in Northwestern Polytechnical University (NPU) under Grants XQ201911, in part by the Seed Foundation of Innovation and Creation for Graduate Students in NPU under Grants ZZ2019029, and in part by the Project for Graduate Innovation team of NPU. We appreciate the efforts devoted by LUNA16 challenge organizers to collect and share the data.
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Zhang, F., Xie, Y., Xia, Y., Zhang, Y. (2019). A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection. In: Wang, Q., et al. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART MIL3ID 2019 2019. Lecture Notes in Computer Science(), vol 11795. Springer, Cham. https://doi.org/10.1007/978-3-030-33391-1_9
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