Ying J. Zhu

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Robust tracking of deformable object like catheter or vascular structures in X-ray images is an important technique used in image guided medical interventions for effective motion compensation and dynamic multi-modality image fusion. Tracking of such anatomical structures and devices is very challenging due to large degrees of appearance changes, low(More)
Family based association study (FBAS) has the advantages of controlling for population stratification and testing for linkage and association simultaneously. We propose a retrospective multilevel model (rMLM) approach to analyze sibship data by using genotypic information as the dependent variable. Simulated data sets were generated using the simulation of(More)
Traditionally, survival estimates following liver transplantation (LT) of hepatocellular carcinoma (HCC) patients were calculated as survival from the surgery date, but future survival probabilities can change over time and conditional disease-free survival (CDFS) may provide patients and clinicians with more accurate prognostic information. This study(More)
Liver cancer is the third leading cause of cancer-associated mortality worldwide. Recurrence and metastasis are the major factors affecting the prognosis; thus, investigation of the underlying molecular mechanisms of invasion and metastasis, and detection of novel drug target may improve the mortality rate of liver cancer patients. Chromosome region(More)
Tracking of curvilinear structures (CS), such as vessels and catheters, in X-ray images has become increasingly important in recent interventional applications. However, CS is often barely visible in low-dose X-ray due to overlay of multiple 3D objects in a 2D projection, making robust and accurate tracking of CS very difficult. To address this challenge,(More)
Vessel segmentation is an important problem in medical image analysis and is often challenging due to large variations in vessel appearance and profiles, as well as image noises. To address these challenges, we propose a solution by combining heterogeneous context-aware features with a discriminative learning framework. Our solution is characterized by(More)
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