Abstract
CT Colonography (CTC) has emerged as a mainstream clinical practice of colonic cancer screening and diagnosis. One of the most critical problems is to increase compliance with CTC examinations via minimal bowel preparation (i.e., weak faecal-tagging), which nevertheless causes much lower signal-noise-ratio than conventional preparation.
In this paper, we present a new algorithm pipeline of electronically cleansing tagging materials in CTC under reduced oral contrast dose. Our method has the following steps: 1, robust structure parsing to generate a list of volume regions of interest (ROIs) of tagging material (avoiding bone erosion); 2, effectively locating local tagging-air (AT) transitional surface regions; 3, a novel discriminative-generative algorithm to learn the higher-order image appearance model in AT using 3D Markov Random Fields (MRF); 4, accurate probability density function based voxel labeling corresponding to semantic classes. Validated on 26 weak faecal-tagging CTC cases from 3 medical sites, our method yields better visualization clarity and readability compared with the previous approach [1]. The whole system computes efficiently (e.g., < 40 seconds for CT images of 512×512×1000 +).
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References
Wang, Z., Liang, Z., et al.: An Improved Electronic Colon Cleansing Method for Detection of Colonic Polyps by Virtual Colonoscopy. IEEE Trans. Biomed. Engi. 53, 1635–1646 (2006)
Serlie, I., Vos, F., Truyen, R., Post, F., Stoker, J., van Vliet, L.: Electronic Cleansing for Computed Tomography (CT) Colonography Using a Scale-Invariant Three-Material Model. IEEE Trans. Biomed. Engi. 57, 1306–1317 (2010)
Liedenbaum, M., Denters, M., Zijta, F., van Ravesteijn, V., Bipat, S., Vos, F., Dekker, E., Stoker, J.: Reducing the oral contrast dose in CT colonography: evaluation of faecal tagging quality and patient acceptance. Clin. Radiol., 30–37 (2010)
Lu, L., Wolf, M., Liang, J., Dundar, M., Bi, J., Salganicoff, M.: A Two-level Approach Towards Semantic Colon Segmentation: Removing Extra-colonic Findings. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 1009–1016. Springer, Heidelberg (2009)
Wu, T., Jian, B., Zhou, X.S.: Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol. 7009, pp. 126–133. Springer, Heidelberg (2011)
Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. Pat. Anal. Mach. Intell. 28, 1465–1479 (2006)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Trans. Pat. Anal. Mach. Intell. 24(5), 603–619 (2002)
Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589–1596 (2005)
Cover, T.M., Thomas, J.A.: Elements of information theory (1991)
Konishi, S., Yuille, A., Coughlan, J., Zhu, S.: Statistical edge detection: Learning and evaluating edge cues. IEEE Trans. Pat. Anal. Mach. Intell. 25, 57–74 (2003)
Lu, L., et al.: Hierarchical learning for tubular structure parsing in medical imaging: A study on coronary arteries using 3D CT Angiography. In: ICCV (2009)
Boykov, Y., Funka-Lea, G.: Graph Cuts and Efficient N-D Image Segmentation. Int. J. Comp. Vis. 70(2), 109–131 (2006)
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Lu, L., Jian, B., Wu, D., Wolf, M. (2013). A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds) Machine Learning in Medical Imaging. MLMI 2013. Lecture Notes in Computer Science, vol 8184. Springer, Cham. https://doi.org/10.1007/978-3-319-02267-3_8
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DOI: https://doi.org/10.1007/978-3-319-02267-3_8
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