Abstract
Kidney lesions are important extracolonic findings at computed tomographic colonography (CTC). However, kidney lesion detection on non-contrast CTC images poses significant challenges due to low image contrast with surrounding tissues. In this paper, we treat the kidney surface as manifolds in Riemannian space and present an intrinsic manifold diffusion approach to identify lesion-caused protrusion while simultaneously removing geometrical noise on the manifolds. Exophytic lesions (those that deform the kidney surface) are detected by searching for surface points with local maximum diffusion response and using the normalized cut algorithm to extract them. Moreover, multi-scale diffusion response is a discriminative feature descriptor for the subsequent classification to reduce false positives. We validated the proposed method and compared it with a baseline method using shape index on CTC datasets from 49 patients. Free-response receiver operating characteristic analysis showed that at 7 false positives, the proposed method achieved 87% sensitivity while the baseline method achieved only 22% sensitivity. The proposed method showed far fewer false positives compared with the baseline method which makes it feasible for clinical practice.
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Bay, H., Ess, A., Tuytelaars, T., Gool, L.: Surf: Speeded up robust features. computer vision and image understanding. CVIU 110(3), 346–359 (2008)
Chang, C., Lin, C.: Libsvm: a library for support vector machines. ACM Trans. Intell Sys. Tech. 2(27), 1–27 (2011)
Chung, F.: Spectral Graph Theory (CBMS Regional Conference Series in Mathematics, No. 92). American Mathematical Society (1996)
Desbrun, M., Meyer, M., Schrder, P., Barr, A.H.: Implicit fairing of irregular meshes using diffusion and curvature flow (1999)
Irishina, N., Moscoso, M., Dorn, O.: Microwave imaging for early breast cancer detection using a shape-based strategy. IEEE Trans. Biomed. Eng. 56(4), 1143–1153 (2009)
Koenderink, J., Doorn, A.: Surface shape and curvature scales. Image and Vision Computing 10(8), 557–564 (1992)
Lai, Z., Hu, J., Liu, C., Taimouri, V., Pai, D., Zhu, J., Xu, J., Hua, J.: Intra-patient supine-prone colon registration in ct colonography using shape spectrum. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 332–339. Springer, Heidelberg (2010)
Linguraru, M., Wang, S., Shah, F.: et al: Automated noninvasive classification of renal cancer on multi-phase ct. Med. Phys. 38, 5738–5746 (2011)
Liu, J., Linguraru, M.G., Wang, S., Summers, R.M.: Automatic segmentation of kidneys from non-contrast ct images using efficient belief propagation. In: SPIE Medical Imaging (2013)
Lorensen, W., Cline, H.: Marching cubes: A high resolution 3d surface construction algorithm. In: Computer Graphics, vol. 21, pp. 163–169 (1987)
Pickhardt, P., Hanson, M., Vanness, D.: et al: Unsuspected extracolonic findings at screening ct colonography: clinical and economic impact. Radiology 249, 151–159 (2008)
Reuter, M., Wolter, F.E., Peinecke, N.: Laplace-beltrami spectra as “shape-dna” of surfaces and solids. Computer-Aided Design 38(4), 342–366 (2006)
Seo, S., Chung, M.K., Vorperian, H.K.: Heat kernel smoothing using laplace-beltrami eigenfunctions. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 505–512. Springer, Heidelberg (2010)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. PAMI 22(8), 888–905 (2000)
Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature-based on heat diffusion. Comp. Graph. Forum 28, 1383–1392 (2008)
Yoshida, H., Nappi, J., MacEneaney, P.: et al: Computer-aided diagnosis scheme for detection of polyps at ct colonography. Radio Graphics 22, 963–979 (2002)
Zalis, M.E.: et al: Ct colonography reporting and data system: A consensus proposal. Radiology 236, 3–9 (2005)
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Liu, J., Wang, S., Yao, J., Linguraru, M.G., Summers, R.M. (2013). Manifold Diffusion for Exophytic Kidney Lesion Detection on Non-contrast CT Images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40811-3_43
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DOI: https://doi.org/10.1007/978-3-642-40811-3_43
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