Lung Nodule Segmentation Using 3-Dimensional Convolutional Neural Networks

1. Armato III, 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. Med. Phys. 38(2), 915–931 (2011)

   Article  Google Scholar 

2. Cha, J., Farhangi, M.M., Dunlap, N., Amini, A.A.: Segmentation and tracking of lung nodules via graph-cuts incorporating shape prior and motion from 4D CT. Med. Phys. 45(1), 297–306 (2018)

   Article  Google Scholar 

3. 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)

   Google Scholar 

4. Keshani, M., Azimifar, Z., Tajeripour, F., Boostani, R.: Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system. Comput. Biol. Med. 43(4), 287–300 (2013)

   Article  Google Scholar 

5. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)

   Google Scholar 

6. Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. In: ACM siggraph computer graphics, vol. 21, pp. 163–169. ACM (1987)

   Google Scholar 

7. Lowekamp, B.C., Chen, D.T., Ibáñez, L., Blezek, D.: The design of simpleTK. Front. Neuroinform. 7, 45 (2013)

   Article  Google Scholar 

8. Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 3D Vision (3DV), 2016 Fourth International Conference on, pp. 565–571. IEEE (2016)

   Google Scholar 

9. Nam, C.-M., Kim, J., Lee, K.J.: Lung nodule segmentation with convolutional neural network trained by simple diameter information (2018)

   Google Scholar 

10. Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in PyTorch. In: NIPS-W (2017)

    Google Scholar 

11. Roerdink, J.B., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41(12), 187–228 (2000)

    Google Scholar 

12. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, pp. 234–241. Springer (2015)

    Google Scholar 

13. Siegel, R., Miller, K., Jemal, A.: Cancer statistics. CA: Cancer J. Clin. 68, 7–30 (2017)

    Google Scholar 

14. Skourt, B.A., El Hassani, A., Majda, A.: Lung CT image segmentation using deep neural networks. Procedia Comput. Sci. 127, 109–113 (2018)

    Article  Google Scholar 

15. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9 (2015)

    Google Scholar 

16. Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 29 (2015)

    Article  Google Scholar 

17. Tan, Y., Schwartz, L.H., Zhao, B.: Segmentation of lung lesions on CT scans using watershed, active contours, and markov random field. Med. Phys. 40(4) (2013)

    Article  Google Scholar 

18. Wang, S., Zhou, M., Gevaert, O., Tang, Z., Dong, D., Liu, Z., Tian, J.: A multi-view deep convolutional neural networks for lung nodule segmentation. In: Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, pp. 1752–1755. IEEE (2017)

    Google Scholar 

19. Wang, S., Zhou, M., Liu, Z., Liu, Z., Gu, D., Zang, Y., Dong, D., Gevaert, O., Tian, J.: Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation. Med. Image Anal. 40, 172–183 (2017)

    Article  Google Scholar 

20. Wu, B., Zhou, Z., Wang, J., Wang, Y.: Joint learning for pulmonary nodule segmentation, attributes and malignancy prediction. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1109–1113. IEEE (2018)

    Google Scholar 

21. Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network (2015). arXiv:1505.00853

22. Yin, Y., Hoffman, E.A., Ding, K., Reinhardt, J.M., Lin, C.-L.: A cubic b-spline-based hybrid registration of lung CT images for a dynamic airway geometric model with large deformation. Phys. Med. Biol. 56(1), 203 (2010)

    Article  Google Scholar 

23. Zou, K.H., Warfield, S.K., Bharatha, A., Tempany, C.M., Kaus, M.R., Haker, S.J., Wells III, W.M., Jolesz, F.A., Kikinis, R.: Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Acad. Radiol. 11(2), 178–189 (2004)

    Article  Google Scholar 

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