Segmentation of liver tumor using efficient global optimal tree metrics graph cuts

Authors:
Ruogu Fang;Ramin Zabih;Ashish Raj;Tsuhan Chen
Affiliations:
Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY;Department of Computer Science, Cornell University, Ithaca, NY;Department of Radiology, Cornell University, New York City, NY;Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY
Venue:
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
Year:
2011

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Abstract

We propose a novel approach that applies global optimal tree-metrics graph cuts algorithm on multi-phase contrast enhanced contrast enhanced MRI for liver tumor segmentation. To address the difficulties caused by low contrasted boundaries and high variability in liver tumor segmentation, we first extract a set of features in multi-phase contrast enhanced MRI data and use color-space mapping to reveal spatial-temporal information invisible in MRI intensity images. Then we apply efficient tree-metrics graph cut algorithm on multi-phase contrast enhanced MRI data to obtain global optimal labeling in an unsupervised framework. Finally we use tree-pruning method to reduce the number of available labels for liver tumor segmentation. Experiments on real-world clinical data show encouraging results. This approach can be applied to various medical imaging modalities and organs.