Abstract
In this paper, we presented a novel hybrid classification based method for fully automated polyp segmentation in colonoscopy video frames. It contains two main steps: initial region proposals generation and regions refinement. Both machine learned features and hand crafted features are taken into account for polyp segmentation. More specifically, the hierarchical features of polyps are learned by fully convolutional neural network (FCN), while the context information related to the polyp boundaries is modeled by texton patch representation. The FCN provides pixel-wise prediction and initial polyp region candidates. Those candidates are further refined by patch-wise classification using texton based spatial features and a random forest classifier. The segmentation results are evaluated on a publicly available CVC-ColonDB database. On average, our method achieves 97.54% of accuracy, 75.66% of sensitivity, 98.81% of specificity and DICE of 0.70%. The fast execution time (0.16 s/frame) demonstrates the promise of our method to be used in real-time clinical colonoscopic examination.
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References
Winawer, S.J., Zauber, A.G., Ho, M.N., Obrien, M.J., Gottlieb, L.S., Sternberg, S.S., Waye, J.D., Schapiro, M., Bond, J.H., Panish, J.F., Ackroyd, F., Shike, M., Kurtz, R.C., Hornsbylewis, L., Gerdes, H., Stewart, E.T.: Prevention of colorectal-cancer by colonoscopic polypectomy. New Engl. J. Med. 329, 1977–1981 (1993)
Lieberman, D.: Quality and colonoscopy: a new imperative. Gastrointest. Endosc. 61, 392–394 (2005)
Tajbakhsh, N., Gurudu, S.R., Liang, J.: A classification-enhanced vote accumulation scheme for detecting colonic polyps. In: Yoshida, H., Warfield, S., Vannier, M.W. (eds.) ABD-MICCAI 2013. LNCS, vol. 8198, pp. 53–62. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41083-3_7
Hwang, S., Oh, J., Tavanapong, W., Wong, J., de Groen, P.C.: Polyp detection in colonoscopy video using elliptical shape feature. In: IEEE International Conference on Image Processing, pp. 1029–1032 (2007)
Bernal, J., Sanchez, J., Vilarino, F.: Towards automatic polyp detection with a polyp appearance model. Pattern Recogn. 45, 3166–3182 (2012)
Li, P., Chan, K.L., Krishnan, S.M.: Learning a multi-size patch-based hybrid kernel machine ensemble for abnormal region detection in colonoscopic images. In: Proceedings of CVPR, pp. 670–675. IEEE (2005)
Park, S.Y., Sargent, D., Spofford, I., Vosburgh, K.G., A-Rahim, Y.: A colon video analysis framework for polyp detection. IEEE Trans. Bio-Med. Eng. 59, 1408–1418 (2012)
Bae, S.H., Yoon, K.J.: Polyp detection via imbalanced learning and discriminative feature learning. IEEE Trans. Bio-Med. Imaging 34, 2379–2393 (2015)
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automatic polyp detection using global geometric constraints and local intensity variation patterns. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 179–187. Springer, Cham (2014). doi:10.1007/978-3-319-10470-6_23
Tajbakhsh, N., Gurudu, S.R., Liang, J.M.: Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. In: International symposium on Biomed Imaging, pp. 79–83 (2015)
Tajbakhsh, N., Gurudu, S.R., Liang, J.M.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imaging 35, 630–644 (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Ieee International Symposium on Circuits and systems, pp. 253–256 (2010)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: CORR abs/1409.1556 (2014)
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Jianming, L.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35, 1299–1312 (2016)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587 (2014)
Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vis. 62, 61–81 (2005)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vis. 43, 29–44 (2001)
Daugman, J.G.: Uncertainty relation for resolution in space, spatial-frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. 2, 1160–1169 (1985)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)
Vedaldi, A., Lenc, K.: MatConvNet convolutional neural networks for MATLAB. In: MM 2015: Proceedings of the 2015 Acm Multimedia Conference, pp. 689–692 (2015)
Acknowledgment
This research was supported by Cancer Research UK (CRUK) funded project “Bowels - inside out” (A22873).
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Zhang, L., Dolwani, S., Ye, X. (2017). Automated Polyp Segmentation in Colonoscopy Frames Using Fully Convolutional Neural Network and Textons. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_62
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