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)
Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical images remains challenging. This work investigates efficient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With(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)
A cascade of fully convolutional neural networks is proposed to segment multi-modality MR images with brain tumor into background and three subregions: enhanced tumor core, whole tumor and tumor core. The cascade is designed to decompose the multi-class segmentation into a sequence of three binary segmentations according to the subregion hierarchy.(More)
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have shown to be state-of-the-art automatic segmentation methods while the result still needs to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive(More)
Liver surgery planning system plays an important role in achieving the optimized surgery plan in Living Donor Liver Transplantation (LDLT). Segmentation of liver is a very challenging component in liver surgery planning systems. Patient-specific shape prior is of great significance in improving the robustness of liver segmentation. However, complex liver(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)