Abstract
Often in neurosurgical planning a dual spin echo acquisition is performed that yields proton density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. The development of vessel segmentation algorithms for PD images is of general interest since this type of acquisition is widespread and is entirely noninvasive. Whereas vessels are signaled by black blood contrast in such images, extracting them is a challenge because other anatomical structures also yield similar contrasts at their boundaries. In this paper, we present a novel multi-scale geometric flow for segmenting vasculature from standard MRI which can also be applied to the easier cases of angiography data. We first apply Frangi’s vesselness measure [3] to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which is orthogonal to vessel boundaries so that the flux maximizing geometric flow algorithm of [14] can be used to recover them. We perform a quantitative cross validation on PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, all obtained for the same subject. A significant finding is that whereas more than 80% of the vasculature recovered from the angiographic data is also recovered from the PD volume, over 25% of the vasculature recovered from the PD volume is not detected in the TOF data. Thus, the technique can be used not only to improve upon the results obtained from angiographic data but also as an alternative when such data is not available.
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
Bullitt, E., Aylward, S., Liu, A., Stone, J., Mukherjee, S.K., Coffey, C., Gerig, G., Pizer, S.M.: 3d graph description of the intracerebral vasculature from segmented mra and tests of accuracy by comparison with x-ray angiograms. Information Processing in Medical Imaging, 308–321 (1999)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Frangi, A., Niessen, W., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Grayson, M.: The heat equation shrinks embedded plane curves to round points. Journal of Differential Geometry 26, 285–314 (1987)
Koller, T.M., Gerig, G., Székely, G., Dettwiler, D.: Multiscale detection of curvilinear structures in 2-d and 3-d image data. In: International Conference On Computer Vision, pp. 864–869 (1995)
Krissian, K., Malandain, G., Ayache, N.: Model-based detection of tubular structures in 3d images. Computer Vision and Image Understanding 80(2), 130–171 (2000)
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision 30(2), 77–116 (1998)
Lorenz, C., Carlsen, I., Buzug, T., Fassnacht, C., Weese, J.: Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2d and 3d medical images. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 233–242. Springer, Heidelberg (1997)
Lorigo, L.M., Faugeras, O.D., Grimson, E.L., Keriven, R., Kikinis, R., Nabavi, A., Westin, C.-F.: Curves: Curve evolution for vessel segmentation. Medical Image Analysis 5, 195–206 (2001)
McInerney, T., Terzopoulos, D.: T-snakes: Topology adaptive snakes. Medical Image Analysis 4, 73–91 (2000)
Osher, S.J., Sethian, J.A.: Fronts propagating with curvature dependent speed: Algorithms based on hamilton-jacobi formulations. Journal of Computational Physics 79, 12–49 (1988)
Ostergaard, L., Larsen, O., Goualher, G., Evans, A., Collins, D.: Extraction of cerebral vasculature from mri. In: 9th Danish Conference on Pattern Recognition and Image Analysis (2000)
Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R.: 3d multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis 2(2), 143–168 (1998)
Vasilevskyi, A., Siddiqi, K.: Flux maximizing geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(12), 1–14 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Descoteaux, M., Collins, L., Siddiqi, K. (2004). Geometric Flows for Segmenting Vasculature in MRI: Theory and Validation. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30135-6_61
Download citation
DOI: https://doi.org/10.1007/978-3-540-30135-6_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22976-6
Online ISBN: 978-3-540-30135-6
eBook Packages: Springer Book Archive