Abstract
In this chapter, we present an accurate automated 3D liver segmentation scheme for measuring liver volumes in MR images. Our scheme consisted of five steps. First, an anisotropic diffusion smoothing filter was applied to T1-weighted MR images of the liver in the portal-venous phase to reduce noise while preserving the liver boundaries. An edge enhancer and a nonlinear gray-scale converter were applied to enhance the liver boundary. This boundary-enhanced image was used as a speed function for a 3D fast marching algorithm to generate an initial surface that roughly approximated the liver shape. A 3D geodesic active contour segmentation algorithm refined the initial surface so as to more precisely determine the liver boundary. The liver volume was calculated based on the refined liver surface. The MR liver volumetry based on our automated scheme agreed excellently with “gold-standard” manual volumetry (intra-class correlation coefficient was 0.98) and required substantially less completion time (our processing time of 1 vs. 24 min/case in manual segmentation).
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Acknowledgments
The authors are grateful to members in the Suzuki Laboratory in the Department of Radiology at the University of Chicago for their valuable comments. This work was partly supported by the NIH S10 RR021039, P30 CA14599, and Vietnam Education Foundation.
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Huynh, H.T., Karademir, I., Oto, A., Suzuki, K. (2014). Liver Volumetry in MRI by Using Fast Marching Algorithm Coupled with 3D Geodesic Active Contour Segmentation. In: Suzuki, K. (eds) Computational Intelligence in Biomedical Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7245-2_6
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DOI: https://doi.org/10.1007/978-1-4614-7245-2_6
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