Abstract
Thin-slice computer tomography provides high-resolution images that facilitate the diagnosis of early-stage lung cancer. However, the sheer size of the CT volumes introduces variability in radiological readings, driving the need for automated detection systems. The main contribution of this paper is a technique for combining geometric and intensity models with the analysis of local curvature for detecting pulmonary lesions in CT. The local shape at each voxel is represented via the principal curvatures of its associated isosurface without explicitly extracting the isosurface. The comparison of these curvatures to values derived from analytical shape models is then used to label the voxel as belonging to particular anatomical structures, e.g., nodules or vessels. The algorithm was evaluated on 242 CT exams with expert-determined ground truth. The performance of the algorithm is quantified by free-response receiver-operator characteristic curves, as well as by its potential for improvement in radiologist sensitivity.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Mulshine, J.L., Smith, R.A.: Lung cancer 2: Screening and early diagnosis of lung cancer. Thorax 57, 1071–1078 (2002)
McCulloch, C.C., Yankelevitz, D., Henschke, C., Patel, S., Kazerooni, E., Sirohey, S.: Reader variability and computer aided detection of suspicious lesions in low-dose CT lung screening exams. Radiology 226, 37A (2003)
Brown, M.S., Goldin, J.G., Suh, R.D., McNitt-Gray, M.F., Sayre, J.W., Aberle, D.R.: Lung micronodules: Automated method for detection at thin-section CT — initial experience. Radiology 226, 256–262 (2003)
McCulloch, C.C., Kaucic, R.A., Mendonça, P.R.S., Walter, D.J., Avila, R.S.: Model-based detection of lung nodules in computed tomography exams. Academic Radiology 11, 258–266 (2004)
Paik, D.S., Beaulieu, C.F., Rubin, G.D., Acar, B., Jeffrey, J.R.B., Yee, J., Dey, J., Napel, S.: Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE Trans. Medical Imaging 23, 661–675 (2004)
Farag, A.A., El-Baz, A., Gimel’farb, G.G., Falk, R., Hushek, S.G.: Automatic detection and recognition of lung abnormalities in helical CT images using deformable templates. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 856–864. Springer, Heidelberg (2004)
ELCAP: International early cancer action program — Protocol (2003), http://icscreen.med.cornell.edu/ielcap.pdf
Sato, Y., Westin, C., Bhalerao, A., Nakajima, S., Shiraga, N., Tamura, S., Kikinis, R.: Tissue classification based on 3D local intensity structures for volume rendering. IEEE Trans. Visualization and Computer Graphics 6, 160–180 (2000)
Yoshida, H., Näppi, J.: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans. Medical Imaging 20, 1261–1274 (2001)
Vos, F.M., Serlie, I.W.O., van Gelder, R.E., Post, F.H., Truyen, R., Gerritsen, F.A., Stoker, J., Vossepoel, A.M.: A new visualization method for virtual colonoscopy. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 645–654. Springer, Heidelberg (2001)
O’Neill, B.: Elementary Differential Geometry. Academic Press, New York (1966)
Forsyth, D.A.: Shape from texture and integrability. In: Proc. 8th Int. Conf. on Computer Vision, vol. II, Vancouver, Canada, pp. 447–452 (2001)
Yankelevitz, D.F., Reeves, A.P., Kostis, W.J., Zhao, B., Henschke, C.I.: Small pulmonary nodules: Volumetrically determined growth rates based on CT evaluation. Radiology 217, 251–256 (2000)
Ibáñez, L., Schroeder, W., Ng, L., Cates, J.: The ITK Software Guide. Kitware Inc. (2003)
Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model based detection of tubular structures in 3D images. Computer Vision and Image Understanding 80, 130–171 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mendonça, P.R.S., Bhotika, R., Sirohey, S.A., Turner, W.D., Miller, J.V., Avila, R.S. (2005). Model-Based Analysis of Local Shape for Lesion Detection in CT Scans. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_85
Download citation
DOI: https://doi.org/10.1007/11566465_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29327-9
Online ISBN: 978-3-540-32094-4
eBook Packages: Computer ScienceComputer Science (R0)