Abstract
Acute aortic dissection is a life-threatening condition and must be diagnosed and treated promptly. For treatment planning the reliable identification of the true and false lumen is crucial. However, a fully automatic Computer Aided Diagnosis system capable to display the different lumens in an easily comprehensible and timely manner is still not available.
In this paper we present a method that segments the entire aorta and then identifies the two lumens separated by the dissection membrane. The algorithm misdetected part of the membrane in only one of the 15 cases tested, where the aorta has not been significantly altered by the presence of aneurisms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sollie, B.H.: Automatic segmentation and registration of CT and US images of abdominal aortic aneurysm using ITK. Masterthesis: Norwegian University of Science and Technology, Norway (2002)
Baissalov, R., Sandison, G.A., Donnelly, B.J., et al.: Suppression of high-density artefacts in X-ray CT images using temporal digital subtraction with application to cryotherapy. Phys. Med. Biol. 45, 53–59 (2000)
Behrens, T., Rohr, K., Stiehl, H.S.: Robust segmentation of tubular structures in 3-D medical images by parametric object detection and traking. IEEE Trans. Syst. Man Cybern. B 33, 554–561 (2003)
Kovács, T., Cattin, P., Alkadhi, H., Wildermuth, S., Székely, G.: Automatic segmentation of the vessel lumen from 3D CTA images of aortic dissection. In: Bildverarbeitung für die Medizin, pp. 161–165 (2006)
Sato, Y., Westin, C., Bhalerao, A., et al.: Tissue classification based on 3d local intensity structures for volume rendering. IEEE Trans. Vis. Comput. Graph. 6, 160–180 (2000)
Krissian, K., Malandain, G., Ayache, N., et al.: Model-based detection of tubular structures in 3D images. Comput. Vis. Image Underst. 80, 130–171 (2000)
Frangi, A.F., Niessen, W.J., Vincken, K.L., et al.: 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)
Descoteaux, M., Audette, M., Chinzei, K., et al.: Bone enhancement filtering: Application to sinus bone segmentation and simulation of pituitary surgery. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 9–16. Springer, Heidelberg (2005)
Koller, T., Gerig, G., Szekely, G., et al.: Multiscale detection of curvilinear structures in 2D and 3D image data. In: ICCV, pp. 864–869 (1995)
Felkel, P., Wegenkittl, R., Kanitsar, A.: Vessel tracking in peripheral cta datasets – an overview. In: SCCG 2001, p. 232 (2001)
Sethian, J.: Level Set Methods and Fast Marching Methods, ch. 16. Cambridge Press, Cambridge (1999)
Brown, J., Montgomery, K., Latombe, J.C., et al.: A microsurgery simulation system. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 137–144. Springer, Heidelberg (2001)
Zsemlye, G.: Shape Prediction from Partial Information. PhD thesis, Computer Vision Laboratory, ETH Zurich, Switzerland (2005)
Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow, pp. 359–369 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kovács, T., Cattin, P., Alkadhi, H., Wildermuth, S., Székely, G. (2006). Automatic Segmentation of the Aortic Dissection Membrane from 3D CTA Images. In: Yang, GZ., Jiang, T., Shen, D., Gu, L., Yang, J. (eds) Medical Imaging and Augmented Reality. MIAR 2006. Lecture Notes in Computer Science, vol 4091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11812715_40
Download citation
DOI: https://doi.org/10.1007/11812715_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37220-2
Online ISBN: 978-3-540-37221-9
eBook Packages: Computer ScienceComputer Science (R0)