Abstract
The segmentation and classification of the major intra-hepatic blood vessels along with the segmentation and analysis of hepatic tumors are critical for patient specific models of the diseased liver. Additionally, the accurate identification of liver anatomical segments can assist in the clinical assessment of the risks and benefits of hepatic interventions. We propose a novel 4D graph-based method to segment hepatic vasculature and tumors. The algorithm uses multi-phase CT images to model the differential enhancement of the liver structures and Hessian-based shape likelihoods to avoid the common pitfalls of graph cuts with undersegmentation and intensity heterogeneity. A hybrid classification step based on post-order walks of a graph identifies the right, middle and left hepatic, and portal veins. Veins are tracked using the graph representation and planes fitted to the vessel segments. The method allows the detection of all hepatic tumors and identification of the liver segments with 87.8% accuracy.
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Pamulapati, V., Venkatesan, A., Wood, B.J., Linguraru, M.G. (2012). Liver Segmental Anatomy and Analysis from Vessel and Tumor Segmentation via Optimized Graph Cuts. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2011. Lecture Notes in Computer Science, vol 7029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28557-8_24
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DOI: https://doi.org/10.1007/978-3-642-28557-8_24
Publisher Name: Springer, Berlin, Heidelberg
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