Abstract
Kidney segmentation from dynamic contrast enhanced magnetic resonance images (DCE-MRI) is vital for computer-aided early assessment of kidney functions. To accurately extract kidneys in the presence of inherently inhomogeneous contrast deviations, we control an evolving geometric deformable boundary using specific prior models of kidney shape and visual appearance. Due to analytical estimates from the training data, these priors make the kidney segmentation fast and accurate, offering the prospect of clinical applications. Experiments with 50 DCE-MRI in-vivo data sets confirmed that the proposed approach outperforms three more conventional counterparts.
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CITATION STYLE
Liu, N., Soliman, A., Gimelfarb, G., & El-Baz, A. (2015). Segmenting kidney DCE-MRI using 1st-order shape and 5th-order appearance priors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9349, pp. 77–84). Springer Verlag. https://doi.org/10.1007/978-3-319-24553-9_10
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