Zhennan Yan

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Detecting deception in interpersonal dialog is challenging since deceivers take advantage of the give-and-take of interaction to adapt to any sign of skepticism in an interlocutor's verbal and nonverbal feedback. Human detection accuracy is poor, often with no better than chance performance. In this investigation, we consider whether automated methods can(More)
Accurate segmentation of the 30+ subcortical structures in MR images of whole diseased brains is challenging due to inter-subject variability and complex geometry of brain anatomy. However a clinically viable solution yielding precise segmentation of the structures would enable: 1) accurate, objective measurement of structure volumes many of which are(More)
In general image recognition problems, discriminative information often lies in local image patches. For example, most human identity information exists in the image patches containing human faces. The same situation stays in medical images as well. "Bodypart identity" of a transversal slice-which bodypart the slice comes from-is often indicated by local(More)
Automatic medical image analysis systems often start from identifying the human body part contained in the image; Specifically, given a transversal slice, it is important to know which body part it comes from, namely "slice-based bodypart recognition". This problem has its unique characteristic--the body part of a slice is usually identified by local(More)
Accurate segmentation of whole brain MR images including the cortex, white matter and subcortical structures is challenging due to inter-subject variability and the complex geometry of brain anatomy. However a precise solution would enable accurate, objective measurement of structure volumes for disease quantification. Our contribution is threefold. First(More)
Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic fat-fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver(More)
In this paper, we propose a complete framework that segments lungs from 2D Chest X-Ray (CXR) images automatically and rapidly. The framework includes two main steps: First, given a set of manually segmented training data, some landmark detectors are obtained using learning techniques. Second, using these detected landmarks as boundary indicators, a(More)
In this paper, we present an effective algorithm to construct a 3D shape atlas for the left ventricle of heart from cardiac Magnetic Resonance Image data. We derive a framework that creates a 3D object mesh from a 2D stack of contours, based on geometry processing algorithms and a semi-constrained deformation method. The geometry processing methods include(More)
To achieve the real-time requirement of realistic deformable modelling, it is necessary to use the acceleration techniques such as GPU computing for FEM and employ the feasible hybrid structures in a virtual surgery simulation system. In this paper, we present a linear or nonlinear deformable model of soft tissue. In addition to the efficient meshing and(More)