Abstract
Detecting tubular structures such as airways or vessels in medical images is important for diagnosis and surgical planning. Many state-of-the-art approaches address this problem by starting from the root and progressing towards thinnest tubular structures usually guided by image filtering techniques. These approaches need to be tailored for each application and can fail in noisy or low-contrast regions. In this work, we address these challenges by a two-layer model which consists of a low-level likelihood measure and a high-level measure verifying tubular branches. The algorithm starts by computing a robust measure of tubular presence using a discriminative classifier at multiple image scales. The measure is then used in an efficient multi-scale shortest path algorithm to generate candidate centerline branches and corresponding radii measurements. Finally, the branches are verified by a learning-based indicator function that discards false candidate branches. The experiments on detecting airways in rotational X-ray volumes show that the technique is robust to noise and correctly finds airways even in the presence of imaging artifacts.
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References
Barbu, A., Bogoni, L., Comaniciu, D.: Hierarchical part-based detection of 3D flexible tubes: Application to CT colonoscopy. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 462–470. Springer, Heidelberg (2006)
Bauer, C., Pock, T., Sorantin, E., Bischof, H., Beichel, R.: Segmentation of interwoven 3D tubular tree structures utilizing shape priors and graph cuts. Med. Image Anal. 14(2), 172–184 (2010)
Benmansour, F., Cohen, L.D.: Tubular structure segmentation based on minimal path method and anisotropic enhancement. Int. J. Comput. Vision 92, 192–210 (2011)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, p. 130. Springer, Heidelberg (1998)
Graham, M., Gibbs, J., Cornish, D., Higgins, W.: Robust 3-D airway tree segmentation for image-guided peripheral bronchoscopy. IEEE T. Med. Imaging 29, 982–997 (2010)
Kumar, S., Hebert, M.: A hierarchical field framework for unified context-based classification. In: Proc. Int. Conf. Comput. Vision (2005)
Lesage, D., Angelini, E., Bloch, I., Funka-Lea, G.: Design and study of flux-based features for 3D vascular tracking. In: Proc. Int. Symp. Biomed. Imaging (2009)
Lesage, D., Angelini, E., 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)
Lo, P., Sporring, J., Ashraf, H., Pedersen, J.J., de Bruijne, M.: Vessel-guided airway tree segmentation: A voxel classification approach. Med. Image Anal. 14(4), 527–538 (2010)
Ochs, R., Goldin, J., Abtin, F., Kim, H., Brown, K., Batra, P., Roback, D., McNitt-Gray, M., Brown, M.: Automated classification of lung bronchovascular anatomy in CT using AdaBoost. Med. Image Anal. 11, 315–324 (2007)
Palágyi, K., Sorantin, E., Balogh, E., Kuba, A., Halmai, C., Erdohelyi, B., Hausegger, K.: A sequential 3D thinning algorithm and its medical applications. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 409–415. Springer, Heidelberg (2001)
Rouchdy, Y., Cohen, L.: A geodesic voting method for the segmentation of tubular tree and centerlines. In: Proc. Int. Symp. on Biomed. Imaging, pp. 979–983 (2011)
Schuh, A., Kaftan, J.N., Tietjen, C., O’Donnell, T.P.: Sparse axes-aligned MFlux. In: Workshop on Comp. and Vis. for (Intra-) Vascular Imaging (2011)
Sofka, M., Zhang, J., Zhou, S., Comaniciu, D.: Multiple object detection by sequential Monte Carlo and hierarchical detection network. In: Proc. Int. Conf. Comput. Vision and Pattern Recogn., San Francisco, CA, June 13-18 (2010)
Steger, T., Hosbach, M.: Navigated bronchoscopy using intraoperative fluoroscopy and preoperative CT. In: Proc. Int. Symp. on Biomed. Imaging, pp. 1220–1223 (2012)
Tu, Z.: Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering. In: Proc. Int. Conf. Comput. Vision (2005)
Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Machine Intelligence 32, 1744–1757 (2010)
Türetken, E., Benmansour, F., Fua, P.: Automated reconstruction of tree structures using path classifiers and mixed integer programming. In: Proc. Int. Conf. Comput. Vision and Pattern Recogn., pp. 566–573. IEEE (2012)
Wolf, L., Bileschi, S.: A critical view of context. Int. J. Comput. Vision (2006)
Zheng, Y., Loziczonek, M., Georgescu, B., Zhou, S.K., Vega-Higuera, F., Comaniciu, D.: Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes. In: Proc. SPIE (2011)
Zhou, J., Chang, S., Metaxas, D., Axel, L.: Vascular structure segmentation and bifurcation detection. In: Proc. Int. Symp. on Biomed. Imaging, pp. 872–875 (2007)
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Breitenreicher, D., Sofka, M., Britzen, S., Zhou, S.K. (2013). Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds) Information Processing in Medical Imaging. IPMI 2013. Lecture Notes in Computer Science, vol 7917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38868-2_28
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DOI: https://doi.org/10.1007/978-3-642-38868-2_28
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