Abstract
We propose an automatic approach to aorta segmentation in axial cardiac cine MRI. The segmentation task is formulated as a probabilistic inference problem, seeking for the most probable constellation of aorta locations and shapes in time. To this end, a graphical model is developed that implements the mutual dependencies of the aorta parameters along the cine sequence. Our approach integrates effective means of manual guidance for post-correction in case of erroneous results, requiring only user interaction where necessary. Experiments on a data set of 20 cine sequences showed average Dice coefficients close to the interreader variability while outperforming previous work in the field. Only two post-corrections were required for the entire data set. Results also indicate high stability of our approach w.r.t. re-parameterization.
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References
Völzke H, Alte D, Schmidt CO, et al. Cohort profile: The study of health in pomerania. Int J Epidemiol. 2011;40:294–307.
Rak M, Schnurr AK, Alpers J, et al. Measurement of the aortic diameter in plain axial cardiac cine MRI. Proc BVM. 2015; p. 293–8.
Saur SC, Kühnel C, Boskamp T, et al. Automatic ascending aorta detection in CTA datasets. Proc BVM. 2008; p. 323–7.
Kurkure U, Avila-Montes OC, Kakadiaris IA., Automated segmentation of thoracic aorta in non-contrast CT images. Proc IEEE Int Symp Biomed Imaging. 2008; p. 29–32.
Avila-Montes O, Kurkure U, Nakazato R, et al. Segmentation of the thoracic aorta in non-contrast cardiac CT images. IEEE J Biomed Health Inf. 2013; p. 936–49.
Herment A, Kachenoura N, Lefort M, et al. Automated segmentation of the aorta from phase contrast MR images: validation against expert tracing in healthy volunteers and in patients with a dilated aorta. J Magn Reson Imaging. 2010;31:881–8.
Hegenscheid K, Kühn JP, Völzke H, et al. Whole-body magnetic resonance imaging of healthy volunteers. Pilot study results from the population-based SHIP study. Fortschr Geb Rontgenstr Bildgebenden Verfahren. 2009;181:748–59.
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Rak, M., Alpers, J., Schnurr, AK., Tönnies, KD. (2016). Aorta Segmentation in Axial Cardiac Cine MRI via Graphical Models. In: Tolxdorff, T., Deserno, T., Handels, H., Meinzer, HP. (eds) Bildverarbeitung für die Medizin 2016. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49465-3_39
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DOI: https://doi.org/10.1007/978-3-662-49465-3_39
Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-49465-3
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