Abstract
The current procedure of renal cortex segmentation is subjective and tedious. This investigation is to develop and validate an automated method to segment renal cortex on contrast-enhanced abdominal CT images. The proposed framework consists of four parts: first, an active appearance model (AAM) is built using a set of training images; second, the AAM is refined by live wire (LW) method to initialize the shape and location of the kidney; third, an iterative graph cut-oriented active appearance model (IGC-OAAM) method is applied to segment the kidney; Finally, the identified kidney contour is used as shape constraints for renal cortex segmentation which is also based on IGC-OAAM. The proposed method was validated on a clinical data set of 27 CT angiography images. The experimental results show that: (1) an overall cortex segmentation accuracy with overlap error ≤12.7%, volume difference ≤ 3.9%, average distance ≤ 1.5 mm, root mean square (RMS) distance ≤ 2.8 mm and maximal distance ≤ 19.5 mm could be achieved. (2) The proposed method is highly efficient such that the overall segmentation can be finalized within 2 minutes.
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Chen, X., Zhao, H., Yao, J. (2012). A Fully Automated Framework for Renal Cortex Segmentation. In: Yoshida, H., Hawkes, D., Vannier, M.W. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2012. Lecture Notes in Computer Science, vol 7601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33612-6_22
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DOI: https://doi.org/10.1007/978-3-642-33612-6_22
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