Abstract
Recent advances in Computed Tomography Angiography provide high-resolution 3D images of the vessels. However, there is an inevitable requisite for automated and fast methods to process the increased amount of generated data. In this work, we propose a fast method for vascular skeleton extraction which can be combined with a segmentation algorithm to accelerate the vessel delineation. The algorithm detects central voxels - nodes - of potential vessel regions in the orthogonal CT slices and uses a convolutional neural network (CNN) to identify the true vessel nodes. The nodes are gradually linked together to generate an approximate vascular skeleton. The CNN classifier yields a precision of 0.81 and recall of 0.83 for the medium size vessels and produces a qualitatively evaluated enhanced representation of vascular skeletons.
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References
Lidayová, K., Frimmel, H., Wang, C., Bengtsson, E., Smedby, Ö.: Fast vascular skeleton extraction algorithm. Pattern Recogn. Lett. 76, 67–75 (2016)
Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. (CSUR) 36(2), 81–121 (2004)
Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009)
Charbonnier, J.P., van Rikxoort, E.M., Setio, A.A., Schaefer-Prokop, C.M., van Ginneken, B., Ciompi, F.: Improving airway segmentation in computed tomography using leak detection with convolutional networks. Med. Image Anal. 36, 52–60 (2017)
Merkow, J., Marsden, A., Kriegman, D., Tu, Z.: Dense volume-to-volume vascular boundary detection. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 371–379. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_43
Gülsün, M.A., Funka-Lea, G., Sharma, P., Rapaka, S., Zheng, Y.: Coronary centerline extraction via optimal flow paths and CNN path pruning. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 317–325. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_37
Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)
Tieleman, T., Hinton, G.: Lecture 6.5-RmsProp: divide the gradient by a running average of its recent magnitude. In: COURSERA: Neural Networks for ML (2012)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feed-forward neural networks. In: AISTATS, vol. 9, pp. 249–256 (2010)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Chollet, F.: Keras (2015). https://github.com/fchollet/keras.
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. In: Proceedings of 3rd International Conference on Learning Representations (ICLR) (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv preprint arXiv:1502.03167
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: 27th International Conference on Machine Learning, pp. 807–814 (2010)
Acknowledgements
Lidayová, Frimmel, Bengtsson, and Smedby have been supported by the Swedish Research Council (VR), grant no. 621-2014-6153. Gupta has been supported by Skype IT Academy Stipend Program, EU institutional grant IUT19-11 of Estonian Research Council.
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Lidayová, K., Gupta, A., Frimmel, H., Sintorn, IM., Bengtsson, E., Smedby, Ö. (2017). Classification of Cross-sections for Vascular Skeleton Extraction Using Convolutional Neural Networks. 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_16
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