Abstract
The aim of this study was to clinically evaluate a fully automatic method for segmentation and characterizing the thoracic aorta. From 2010 to 2013, a total of 27 patients were randomly selected for the study. The automatic method was compared with two segmentations manually performed by two independent radiologists amd a commercial software for gauging calibres.
The results of the segmentation measurements showed a Dice Similarity Coefficient of 93.98 \(\pm \) 0.27, a correlation coefficient of 0.875 \(\pm \) 0.012, and an intraclass correlation coefficient of 0.909 \(\pm \) 0.011 for both experts. For the diameter measurements, the values were a Pearson’s Correlation Coefficient of 0.933, and an intraclass correlation of 0.913. The Bland-Altman plots showed no statistically significant differences between the commercial method and the proposed method.
In conclusion, the method developed is capable of automatically calculating the diameters, and the segmentation of the thoracic aorta.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Matsumura, J.S., Cambria, R.P., Dake, M.D., Moore, R.D., Svensson, L.G., Snyder, S.: International controlled clinical trial of thoracic endovascular aneurysm repair with the Zenith TX2 endovascular graft: 1-year results. J. Vasc. Surg. 47, 247–257 (2008)
Garzón, G., Velilla, M.F., Marti, M., Acitores, I., Ybaez, F., Riera, L.: Endovascular stent-graft treatment of thoracic aortic disease. Radiogr. 25, S229–S244 (2005)
Sensier, Y., Akil, Y., Khodabakhsh, P., Naylor, M., Sharpe, R., Walker, J., Hartshorne, T.: The impact of using inner-to-inner wall diameter measurement of abdominal aortic aneurysm. Ultrasound 21(1), 8–11 (2013)
Cayne, N.S., Veith, F.J., Lipsitz, E.C., Ohki, T., Mehta, M., Gargiulo, N., Suggs, W.D., Rozenblit, A., Ricci, Z., Timaran, C.H.: Variability of maximal aortic aneurysm diameter measurements on CT scan: significance and methods to minimize. J. Vasc. Surg. 39(4), 811–815 (2004)
Broeders, I.A.J.M., Blankensteijn, J.D., Olree, M., Mali, W., Eikelboom, B.C.: Preoperative sizing of grafts for transfemoral endovascular aneurysm management: a prospective comparative study of spiral CT angiography, arteriography, and conventional CT images. J. Endovasc. Surg. 4, 252–261 (1997)
Rousseau, H., Chabberta, V., Marachera, M.A., El Aassar, O., Auriol, J., Massabuau, P., Morenoa, R.: The importance of imaging assessment before endovascular repair of thoracic aorta. Eur. J. Vasc. Endovasc. Surg. 38, 408–421 (2009)
Boskamp, T., Math, D., Rinck, D., Link, F., Kummerler, D.B., Phys, D., Stamm, F., Mildenberger, P.: New vessel analysis tool for morphometric quantification and visualization of vessels in CT and MR imaging data sets. Radiogr. 24, 287–297 (2004)
Martínez-Mera, J.A., Tahoces, P.G., Carreira, J.M., Suárez-Cuenca, J.J., Souto, M.: A hybrid method based on level set and 3D region growing for segmentation of thoracic aorta. Comput. Aided Surg. 18, 109–117 (2013)
Martínez-Mera, J.A., Tahoces, P.G., Carreira, J.M., Suárez-Cuenca, J.J., Souto, M.: Automatic characterization of thoracic aortic aneurysms from CT images. Comput. Biol. Med. 57, 74–83 (2014)
Malladi, R., Sethian, J.A., Vemury, B.R.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17, 158–175 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Martínez-Mera, J.A., Tahoces, P.G., Varela-Ponte, R., Suárez-Cuenca, J.J., Souto, M., Carreira, J.M. (2015). Clinical Evaluation of an Automatic Method for Segmentation and Characterization of the Thoracic Aorta with and Without Aneurysm Patients. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_81
Download citation
DOI: https://doi.org/10.1007/978-3-319-19390-8_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19389-2
Online ISBN: 978-3-319-19390-8
eBook Packages: Computer ScienceComputer Science (R0)