Abstract
Liver segmentation is the first data analysis step in computer-aided planning of liver tumor resections. For clinical applicability, the segmentation approach must be able to cope with the high variation in shape and gray-value appearance of the liver. In this article we present a novel segmentation scheme based on a true 3D segmentation refinement concept utilizing a hybrid desktop/virtual reality user interface. The method consists of two main stages. First, an initial segmentation is generated using graph cuts. Second, a segmentation refinement step allows to fix arbitrary segmentation errors. We demonstrate the robustness of our method on ten contrast enhanced liver CT scans and compare it to fifteen other methods. Our segmentation approach copes successfully with the high variation found in patient data sets and allows to produce a segmentation in a time-efficient manner.
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Notes
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Note that graph cut segmentation is not used interactively, as proposed by Boykov et al. in [5], since the behavior of graph cuts is not always intuitive.
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Acknowledgments
This work was supported in part by the Austrian Science Fund (FWF) under Grants P17066-N04 and Y193 and the Doctoral Program Confluence of Vision and Graphics W1209-N15
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Beichel, R., Bauer, C., Bornik, A., Sorantin, E., Bischof, H. (2015). Segmentation of Diseased Livers: A 3D Refinement Approach. In: Paragios, N., Duncan, J., Ayache, N. (eds) Handbook of Biomedical Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09749-7_22
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