Guotai Wang

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PURPOSE To improve the accuracy and the robustness of the segmentation in living donor liver transplantation (LDLT) surgery planning system, the authors present a new segmentation framework that addresses challenges induced by the complex shape variations of patients' livers with cancer. It is designed to achieve the accurate and robust segmentation of(More)
Shape prior plays an important role in accurate and robust liver segmentation. However, liver shapes have complex variations and accurate modeling of liver shapes is challenging. Using large-scale training data can improve the accuracy but it limits the computational efficiency. In order to obtain accurate liver shape priors without sacrificing the(More)
Segmentation of the placenta from fetal MRI is challenging due to sparse acquisition, inter-slice motion, and the widely varying position and shape of the placenta between pregnant women. We propose a minimally interactive framework that combines multiple volumes acquired in different views to obtain accurate segmentation of the placenta. In the first(More)
The recently proposed Sparse Shape Composition (SSC) models shape prior as a sparse linear combination of existing shapes. It is effective to represent complex shape variations, with its ability to capture gross errors and preserve local details. However, SSC has low efficiency when dealing with large-scale training data, which adversely affects its more(More)
Accurately delineating the myocardium from cardiac T2 and delayed enhanced (DE) MRI is a prerequisite to identifying and quantifying the edema and infarcts. The automatic delineation is however challenging due to the heterogeneous intensity distribution of the myocardium. In this paper, we propose a fully automatic method, which combines the complementary(More)
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