Automatic Path Extraction Inside the Aorta from CT Data
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Published
Sep 21, 2024
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Abstract
Segmentation of the aorta is crucial for various medical image analyses, such as the diagnosis of large vessel vasculitis. In this work, we present the extraction of a path inside the aorta from 3D non-contrast CT data using the minimal path approach. We define a suitable potential function to keep the path inside the aorta and as close as possible to the centerline. Using anatomical knowledge, we segment the liver, lungs, and trachea to locate the abdominal aorta, descending aorta, and ascending aorta. Key points are automatically detected by circular Hough Transform using the locations of the liver and trachea. The path inside the aorta is built step by step using the detected key points.
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Allaly, K., & Urbán, J.
(2024).
Automatic Path Extraction Inside the Aorta from CT Data.
Proceedings Of The Conference Algoritmy, , 255 - 263.
Retrieved from http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/2198/1050
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References
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[3] L. D. Cohen and R. Kimmel, Global Minimum for Active Contour Models: A Minimal Path Approach, International Journal of Computer Vision, Volume 24(1), pages 57-78, (1997).
[4] T. Deschamps and L. D. Cohen, Fast extraction of minimal paths in 3D images and applications to virtual endoscopy, Medical Image Analysis, Volume 5, pages 281-299, (2001).
[5] H. Li and A. Yezzi, Vessels as 4-D Curves: Global Minimal 4-D Paths to Extract 3-D Tubular Surfaces and Centerlines, IEEE TRANSACTIONS ON MEDICAL IMAGING, Volume 26, No. 9, SEPTEMBER 2007.
[6] F. Benmansour and L. D. Cohen, Fast Object Segmentation by Growing Minimal Paths from a Single Point on 2D or 3D Images, Journal of Mathematical Imaging and Vision, Volume 33, pages 209-221, (2009).
[7] J. A. Sethian, A fast marching level set method for monotonically advancing fronts, In Proceedings of the Natural Academy of Sciences, Volume 93, pages 1591-1595, February 1996.
[8] H. Zhao, Fast sweeping method for Eikonal equations, MATHEMATICS OF COMPUTATION, Volume 74, Number 250, pages 603-627, May 2004.
[9] Z. Krivá, K. Mikula, N. Peyriéra, B. Rizzi, A. Sarti and O. Stasová, 3D early embryo- genesis image filtering by nonlinear partial differential equations, Medical Image Analysis, Volume 14, pages 510-526, (2010).
[10] A. Sarti and G. Citti, Subjective surfaces and Riemannian mean curvature flow of graphs, In Proceedings of Algoritmy 2000, pages 85-103, (2001).
[11] K. Mikula and M. Remešíková, Finite volume schemes for the generalized subjective surface equation in image segmentation, Kybernetika, Volume 45, Number 4, pages 646-656, (2009).
[12] M. Feuerstein, T. Kitasaka and K. Mori, Automated Anatomical Likelihood Driven Extraction and Branching Detection of Aortic Arch in 3-D Chest CT, In 2nd International Workshop on Pulmonary Image Analysis, pages 49-60, (2009).
[13] T. Kovács, Automatic Segmentation of the Vessel Lumen from 3D CTA Images of Aortic Dissection., PhD. Thesis, ETH Zurich, 2010.
[14] A. Bovik, Handbook of image and video processing, Academic Press, 2000.
[15] J. Canny, A Computational Approach to Edge Detection, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Volume PAMI-8, Number 6, November 1986.
[16] P. W. Furlow and D. J. Mathisen, Surgical anatomy of the trachea, Annals of Cardiothoracic Surgery, Volume 7(2), pages 255-260, (2018).
[17] K. Singhrao, C. L. Dugan, C. Calvin, L. Pelayo, S. S. Yom, J. W. -H. Chan, J. E. Scholey, and L. Singer, Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only ra diation therapy of the head and neck, Journal of Applied Clinical Medical Physics, Volume 25, January 2024.
[18] S. Hu, E. A. Hoffman and J. M. Reinhardt, Automatic Lung Segmentation for Accurate Quantitation of Volumetric X-Ray CT Images, IEEEE Transactions on medical imaging, Volume 20, No. 6, June 2001.
[19] E. M. van Rikxoort and B. van Ginneken, Automatic segmentation of the pulmonary structures in thoracic computed tomography scans: a review, Physics in Medicine and Biology, Volume 58, R187-R220, (2013).
[20] H. K. Yuen, J. Princen, J. Illingworth and J. Kittler, A comparative Study of Hough Transform Methods for Circle finding, Image and Vision Computing, Volume 8(1), pages 71-77, February 1990.
[21] K. Mikula and J. Urbán, A new tangentially stabilized 3D curve evolution algorithm and its application in virtual colonoscopy, Advances in Computational Mathematics, Volume 40, pages 819-837, (2014).
[22] D. A. B. Oliveira, R. Q. Feitosa, and M. M. Correia, Segmentation of liver, its vessels and lesions from CT images for surgical planning, BioMedical Engineering, April 2011.
[23] L. Fernandez-de-Manuel, J. L. Rubio, M. J. Ledesma-Carbayo, J. Pascau, J. M. Tellado, E. Ramon, M. Desco and A. Santos, 3D liver segmentation in preoperative CT images using a level-sets active surface method, In 31st Annual International conference of the IEEE EMBS, September 2009.
[24] K. E. Sengun, Y. T. Cetin, M. S. Guzel, S. Can and G. E. Bostanci, Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study, arXiv:2101.09987, January 2021.