Abstract
In this paper we propose a graph cut based method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using mutual context information. In addition to the conventional log-likelihood penalty, we also include a ‘context penalty’ for the RV by learning its geometrical relationship with respect to the LV. Similarly, the RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context and captures the geometric relationship between the RV and LV. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV, and its robustness to noise and inaccurate segmentations.
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Mahapatra, D., Buhmann, J.M. (2012). Cardiac LV and RV Segmentation Using Mutual Context Information. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_25
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DOI: https://doi.org/10.1007/978-3-642-35428-1_25
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