Computational fluid dynamics simulation of time-resolved blood flow in Budd-Chiari syndrome with inferior vena cava stenosis and its implication for postoperative efficacy assessment.

BACKGROUND: This study aimed to adopt computational fluid dynamics to simulate the blood flow dynamics in inferior vena cava stenosis based on time-dependent patient-specific models of Budd-Chiari syndrome as well as a normal model. It could offer valuable references for a retrospective insight into the underlying mechanisms of Budd-Chiari syndrome pathogenesis as well as more accurate evaluation of postoperative efficacy. METHODS: Three-dimensional inferior vena cava models of Budd-Chiari syndrome patient-specific (preoperative and postoperative) and normal morphology model were reconstructed as per magnetic resonance images using Simpleware. Moreover, computational fluid dynamics of time-resolved inferior vena cava blood flow were simulated using actual patient-specific measurements to reflect time-dependent flow rates. FINDINGS: The assessment of the preoperative model revealed the dramatic variations of hemodynamic parameters of the stenotic inferior vena cava. Moreover, the comparison of the preoperative and postoperative models with the normal model as benchmark showed that postoperative hemodynamic parameters were markedly ameliorated via stenting, with the attenuation of overall velocity and wall shear stress, and the increase of pressure. However, the comparative analysis of the patient-specific simulations revealed that some postoperative hemodynamic profiles still bore some resemblance to the preoperative ones, indicating potential risks of restenosis. INTERPRETATION: Computational fluid dynamics simulation of time-resolved blood flow could reveal the tight correlation between the hemodynamic characteristics and the pathological mechanisms of inferior vena cava stenosis. Furthermore, such time-resolved hemodynamic profiles could provide a quantitative approach to diagnosis, operative regimen and postoperative evaluation of Budd-Chiari syndrome with inferior vena cava stenosis.

Joint Video Object Discovery and Segmentation by Coupled Dynamic Markov Networks.

It is a challenging task to extract segmentation mask of a target from a single noisy video, which involves object discovery coupled with segmentation. To solve this challenge, we present a method to jointly discover and segment an object from a noisy video, where the target disappears intermittently throughout the video. Previous methods either only fulfill video object discovery, or video object segmentation presuming the existence of the object in each frame. We argue that jointly conducting the two tasks in a unified way will be beneficial. In other words, video object discovery and video object segmentation tasks can facilitate each other. To validate this hypothesis, we propose a principled probabilistic model, where two dynamic Markov networks are coupled-one for discovery and the other for segmentation. When conducting the Bayesian inference on this model using belief propagation, the bi-directional message passing reveals a clear collaboration between these two inference tasks. We validated our proposed method in five data sets. The first three video data sets, i.e., the SegTrack data set, the YouTube-objects data set, and the Davis data set, are not noisy, where all video frames contain the objects. The two noisy data sets, i.e., the XJTU-Stevens data set, and the Noisy-ViDiSeg data set, newly introduced in this paper, both have many frames that do not contain the objects. When compared with state of the art, it is shown that although our method produces inferior results on video data sets without noisy frames, we are able to obtain better results on video data sets with noisy frames.