An image-based modeling framework for patient-specific computational hemodynamics

Citations Over TimeTop 1% of 2008 papers

Abstract

We present a modeling framework designed for patient-specific computational hemodynamics to be performed in the context of large-scale studies. The framework takes advantage of the integration of image processing, geometric analysis and mesh generation techniques, with an accent on full automation and high-level interaction. Image segmentation is performed using implicit deformable models taking advantage of a novel approach for selective initialization of vascular branches, as well as of a strategy for the segmentation of small vessels. A robust definition of centerlines provides objective geometric criteria for the automation of surface editing and mesh generation. The framework is available as part of an open-source effort, the Vascular Modeling Toolkit, a first step towards the sharing of tools and data which will be necessary for computational hemodynamics to play a role in evidence-based medicine.

Related Papers

• → Reducing Neural Network Parameter Initialization Into an SMT Problem (Student Abstract)(2021)2 cited
• → A New Initialization Method for Neural Networks with Weight Sharing(2021)2 cited
• → Remarks on the initialization of Caputo derivative(2012)4 cited
• The Distributed Initialization Algorithm Based on Known n MSs(2004)
• → Comparison of Random Weight Initialization to New Weight Initialization CONEXP(2020)