Dr. Guozhi Tao Profile

Dr. Guozhi Tao

at Univ of Texas MD Anderson Cancer Ctr

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Proceedings Article | 13 March 2010 Paper

Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set

Guozhi Tao, Ashish Singh, Luc Bidaut

Proceedings Volume 7623, 76230V (2010) https://doi.org/10.1117/12.844529

KEYWORDS: Liver, Image segmentation, Tissues, Expectation maximization algorithms, Computed tomography, Tumors, Image enhancement, Liver cancer, Image registration, Medical imaging

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In this study, clinically produced multiphase CT volumetric data sets (pre-contrast, arterial and venous enhanced phase) are drawn upon to transcend the intrinsic limitations of single phase data sets for the robust and accurate segmentation of the liver in typically challenging cases. As an initial step, all other phase volumes are registered to either the arterial or venous phase volume by a symmetric nonlinear registration method using mutual information as similarity metric. Once registered, the multiphase CT volumes are pre-filtered to prepare for subsequent steps. Under the assumption that the intensity vectors of different organs follow the Gaussian Mixture model (GMM), expectation maximization (EM) is then used to classify the multiphase voxels into different clusters. The clusters for liver parenchyma, vessels and tumors are combined together and provide the initial liver mask that is used to generate initial zeros level set. Conversely, the voxels classified as non-liver will guide the speed image of the level sets in order to reduce leakage. Geodesic active contour level set using the gradient vector flow (GVF) derived from one of the enhanced phase volumes is then performed to further evolve the liver segmentation mask. Using EM clusters as the reference, the resulting liver mask is finally morphologically post-processed to add missing clusters and reduce leakage. The proposed method has been tested on the clinical data sets of ten patients with relatively complex and/or extensive liver cancer or metastases. A 95.8% dice similarity index when compared to expert manual segmentation demonstrates the high performance and the robustness of our proposed method - even for challenging cancer data sets - and confirms the potential of a more thorough computational exploitation of currently available clinical data sets.

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