Abstract
Heart isolation (separating the heart from the proximity tissues, e.g., lung, liver, and rib cage) is a prerequisite to clearly visualize the coronary arteries in 3D. Such a 3D visualization provides an intuitive view to physicians to diagnose suspicious coronary segments. Heart isolation is also necessary in radiotherapy planning to mask out the heart for the treatment of lung or liver tumors. In this paper, we propose an efficient and robust method for heart isolation in computed tomography (CT) volumes. Marginal space learning (MSL) is used to efficiently estimate the position, orientation, and scale of the heart. An optimal mean shape (which optimally represents the whole shape population) is then aligned with detected pose, followed by boundary refinement using a learning-based boundary detector. Post-processing is further exploited to exclude the rib cage from the heart mask. A large-scale experiment on 589 volumes (including both contrasted and non-contrasted scans) from 288 patients demonstrates the robustness of the approach. It achieves a mean point-to-mesh error of 1.91 mm. Running at a speed of 1.5 s/volume, it is at least 10 times faster than the previous methods.
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References
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Medical Imaging 27(11), 1668–1681 (2008)
Funka-Lea, G., Boykov, Y., Florin, C., Jolly, M.P., Moreau-Gobard, R., Ramaraj, R., Rinck, D.: Automatic heart isolation for CT coronary visualization using graph-cuts. In: Proc. IEEE Int’l Sym. Biomedical Imaging, pp. 614–617 (2006)
Moreno, A., Takemura, C.M., Colliot, O., Camara, O., Bloch, I.: Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images. Pattern Recognition 41(8), 2525–2540 (2008)
van Rikxoort, E.M., Isgum, I., Staring, M., Klein, S., van Ginneken, B.: Adaptive local multi-atlas segmentation: Application to heart segmentation in chest CT scans. In: Proc. of SPIE Medical Imaging (2008)
Lelieveldt, B.P.F., van der Geest, R.J., Rezaee, M.R., Bosch, J.G., Reiber, J.H.C.: Anatomical model matching with fuzzy implicit surfaces for segmentation of thoracic volume scans. IEEE Trans. Medical Imaging 18(3), 218–230 (1999)
Gregson, P.H.: Automatic segmentation of the heart in 3D MR images. In: Canadian Conf. Eletrical and Computer Engineering, pp. 584–587 (1994)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. John Wiley, Chichester (1998)
Tu, Z.: Probabilistic boosting-tree: Learning discriminative methods for classification, recognition, and clustering. In: Proc. Int’l Conf. Computer Vision, pp. 1589–1596 (2005)
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Zheng, Y., Vega-Higuera, F., Zhou, S.K., Comaniciu, D. (2010). Fast and Automatic Heart Isolation in 3D CT Volumes: Optimal Shape Initialization. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_11
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DOI: https://doi.org/10.1007/978-3-642-15948-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15947-3
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