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 Diagnosing system capable to display the different lumens in an easily comprehensible and timely manner is still not available.
In this paper we present the first step towards such a system, namely a method that segments the entire aorta without any user interaction. The method is robust against inhomogeneous distribution of the contrast agent generally seen in dissected aortas, high-density artifacts, and the dissection membrane separating the true and the false lumen.
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References
Verdonck B, Bloch I, Maître H, et al. Accurate Segmentation of Blood Vessels from 3D Medical Images. In: IEEE Int Conf on Image Process; 1996. p. 311–314.
Wink O, Niessen WJ, Viergever MA. Fast Delineation and Visualization of Vessels in 3-D Angiographic Images. IEEE Trans Med Imaging 2000;19(4):337–346.
Katz WT, Merickel MB. Aorta Detection In Magnetic Resonance Images Using Multiple Artificial Neural Networks. In: Annual Int Conf of the IEEE Eng Med Biol Mag; 1990. p. 1302–1303.
Tek H, Akova F, Ayvaci A. Region competition via local watershed operators. In: IEEE Comput Soc Conf on Comput Vis and Pattern Recog; 2005. p. 361–368.
Pohle R, Toennies KD. Segmentation of Medical Images Using Adaptive Region Growing. In: SPIE Med Imaging Conf. vol. 4322; 2001.
Loncarić S, Subasić M, Soratin E. 3-D deformable model for abdominal aortic aneurysm segmentation from CT images. First Int Workshop on Image and Signal Process and Anal 2000.
Baissalov R, Sandison GA, Donnelly BJ, et al. Suppression of high-density artefacts in x-ray CT images using temporal digital subtraction with application to cryotherapy. Phys Med Biol 2000;45:53–59.
McInerney T, Terzopoulos D. Deformable Models in Medical Image Analysis: A Survey. Med Image Anal 1996;1:91–108.
Behrens T, Rohr K, Stiehl HS. Robust Segmentation of Tubular Structures in 3-D Medical Images by Parametric Object Detection and Traking. IEEE Trans Syst Man Cybern B 2003;33:554–561.
Zsemlye Gabriel. Shape Prediction from Partial Information. Ph.D. thesis. Computer Vision Laboratory, ETH Zurich, Switzerland; 2005.
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Kovács, T., Cattin, P., Alkadhi, H., Wildermuth, S., Székely, G. (2006). Automatic Segmentation of the Vessel Lumen from 3D CTA Images of Aortic Dissection. In: Handels, H., Ehrhardt, J., Horsch, A., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2006. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32137-3_33
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DOI: https://doi.org/10.1007/3-540-32137-3_33
Publisher Name: Springer, Berlin, Heidelberg
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