Abstract
Lung cancer is one of the most diagnosable forms of cancer worldwide. The early diagnoses of pulmonary nodules in computed tomography (CT) chest scans are crucial for potential patients. Recent researches have showed that the methods based on deep learning have made a significant progress for the medical diagnoses. However, the achievements on identification of pulmonary nodules are not yet satisfactory enough to be adopted in clinical practice. It is largely caused by either the existence of many false positives or the heavy time of processing. With the development of fully convolutional networks (FCNs), in this study, we proposed a new method of identifying the pulmonary nodules. The method segments the suspected nodules from their environments and then removes the false positives. Especially, it optimizes the network architecture for the identification of nodules rapidly and accurately. In order to remove the false positives, the suspected nodules are reduced using the 2D models. Furthermore, according to the significant differences between nodules and non-nodules in 3D shapes, the false positives are eliminated by integrating into the 3D models and classified via 3D CNNs. The experiments on 1000 patients indicate that our proposed method achieved 97.78% sensitivity rate for segmentation and 90.1% accuracy rate for detection. The maximum response time was less than 30 s and the average time was about 15 s.
This paper has proposed a new method of identifying the pulmonary nodules. The method segments the suspected nodules from CT images and removes the false positives. As shown in the above, the proposed approach consists of three stages. In stage I, raw data are filtered and normalized. The clean normalized data are then segmented in stage II to extract the suspected nodular lesions through 2D FCNs. Stage III is to remove some false positives generated at stage II via 3D CNNs and outputs the final results.
The experiments on 1000 patients indicate that our proposed method has achieved 97.78% sensitivity rate for segmentation and 90.1% accuracy rate for detection. The maximum response time was less than 30 s and the average time was about 15 s.
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References
Siegel RL, Miller KD, Jemal A (2018) . CA: A Cancer J Clinicians 68(1):7. https://doi.org/10.3322/caac.21442. https://onlinelibrary.wiley.com/doi/abs/10.3322/caac.21442
Trajanovski S, Mavroeidis D, Swisher CL, Gebre BG, Veeling B, Wiemker R, Klinder T, Tahmasebi A, Regis SM, Wald C, McKee BJ, MacMahon H, Pien H (2018) Towards radiologist-level cancer risk assessment in ct lung screening using deep learning. arXiv:1804.01901. Cite arxiv:1804.01901Comment: Submitted for publication. 11 pages. 3 pages supplementary material
Dennison D (2018) 2018 state of lung cancer report. https://www.naaccr.org/2018-state-lung-cancer-report/. NAACC Review
Dey R, Lu Z, Hong Y (2018) Diagnostic classification of lung nodules using 3d neural networks. arXiv:1803.07192. Cite arxiv:1803.07192Comment: Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 2018 Copyright c 2018 IEEE
Alakwaa W, Nassef M, Badr A (2017) Int J Adv Comput Sci Appl, 8(8)
Sun T, Wang J, Li X, Lv P, Liu F, Luo Y, Gao Q, Zhu H, Guo X (2013) . Comput Methods Programs Biomed 111(2):519. https://doi.org/10.1016/j.cmpb.2013.04.016. http://www.sciencedirect.com/science/article/pii/S0169260713001387
Choi W-J, Choi T-S (2014) Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput Methods Prog Biomed 113(1):37–54. https://doi.org/10.1016/j.cmpb.2013.08.015
Elbaz A, Elnakib A, Abou EM, Gimel’Farb G, Falk R, Farag A (2013) . Int J Biomed Imag 2013(1):517632
Long J, Shelhamer E, Darrell T (2015) In: IEEE Conference on computer vision and pattern recognition, pp 3431–3440
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) Medical image computing and computer-assisted intervention – MICCAI 2016. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds). Springer International Publishing, Cham, pp 424–432
Oda M, Shimizu N, Oda H, Hayashi Y, Roth HR (2017) Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks. Image Processing
Zhou X, Takayama R, Wang S, Hara T, Fujita H (2017) Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 44(10):5221–5233. https://doi.org/10.1002/mp.12480
Roth HR, Oda H, Hayashi Y, Oda M, Shimizu N, Fujiwara M, Misawa K, Mori K (2017) Hierarchical 3D fully convolutional networks for multi-organ segmentation. CoRR. arXiv:1704.06382
Wu B, Zhou Z, Wang J, Wang Y (2018) In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp 1109–1113. https://doi.org/10.1109/ISBI.2018.8363765
Oda M, Shimizu N, Karasawa K, Nimura Y, Kitasaka T, Misawa K, Fujiwara M, Rueckert D, Mori K (2016) Regression forest-based atlas localization and direction specific atlas generation for pancreas segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9901 LNCS: 556–563. https://doi.org/10.1007/978-3-319-46723-8_64
Fosbinder RD (2011) BA RT(R) essentials of radiologic science. LWW
Wright FW (2001) Radiology of the chest and related conditions. CRC Press
Kazerooni EA, Gross BH (2003) The core curriculum: cardiopulmonary imaging (the core curriculum series). LWW
Chan TF, Vese LA (2001) . IEEE Trans Image Process 10(2):266. https://doi.org/10.1109/83.902291
Comaniciu D, Ramesh V, Meer P (2003) . IEEE Trans Pattern Anal Mach Intell 25(5):564. https://doi.org/10.1109/TPAMI.2003.1195991
Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D (2015) . Med Image Anal 23(1):92
Tianchi (2017) Tianchi medical competition. https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.68877978Zp7JNf&raceId=231601. Accessed 26 Sept (2017)
T. Bvendt01. Lidc-idri (2012). https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI#bb33bb0159ce4700a5a9ad1590697057. Accessed 21 March (2012)
Acknowledgments
The authors would like to thank the data providers of [23] for the testing data sets.
Funding
This work was partially supported by the Natural Science Foundation of China (No. 61572022) and the Ningbo eHealth Project (No. 2016C11024).
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Chen, G., Zhang, J., Zhuo, D. et al. Identification of pulmonary nodules via CT images with hierarchical fully convolutional networks. Med Biol Eng Comput 57, 1567–1580 (2019). https://doi.org/10.1007/s11517-019-01976-1
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DOI: https://doi.org/10.1007/s11517-019-01976-1