Vessel segmentation and blood flow simulation using Level-Sets and Embedded Boundary methods

Deschamps, Thomas; Schwartz, Peter; Trebotich, David; Colella, Phillip; Saloner, David; Malladi, R.

doi:10.1016/j.ics.2004.03.344

Cited by 39 publications

(26 citation statements)

References 7 publications

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“…In this hybrid algorithm we have also explored and modeled short-range interactions between polymers, as well as electrokinetic effects. Finally, our methodology for treating irregular geometry has been coupled to a fast and accurate technique for extracting surface data from patient-specific medical images without loss in anatomical detail [3]. …”

Section: Discussionmentioning

confidence: 99%

“…Our complex geometry approach using embedded boundaries is complementary to a fast and accurate technique to extract surface data from medical images without loss in geometric detail [3,4]. Anatomical surfaces are extracted by means of level-sets methods that accurately model the complex and varying surfaces of pathological objects such as aneurysms and stenoses.…”

Section: Technical Approachmentioning

confidence: 99%

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FY05 LDRD Final Report A Computational Design Tool for Microdevices and Components in Pathogen Detection Systems

Trebotich¹

2006

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We have developed new algorithms to model complex biological flows in integrated biodetection microdevice components. The proposed work is important because the design strategy for the next-generation Autonomous Pathogen Detection System at LLNL is the microfluidic-based Biobriefcase, being developed under the Chemical and Biological Countermeasures Program in the Homeland Security Organization. This miniaturization strategy introduces a new flow regime to systems where biological flow is already complex and not well understood. Also, design and fabrication of MEMS devices is time-consuming and costly due to the current trial-and-error approach. Furthermore, existing devices, in general, are not optimized. There are several MEMS CAD capabilities currently available, but their computational fluid dynamics modeling capabilities are rudimentary at best. Therefore, we proposed a collaboration to develop computational tools at LLNL which will (1) provide critical understanding of the fundamental flow physics involved in bioMEMS devices, (2) shorten the design and fabrication process, and thus reduce costs, (3) optimize current prototypes and (4) provide a prediction capability for the design of new, more advanced microfluidic systems. Computational expertise was provided by Comp-CASC and UC Davis-DAS. The simulation work was supported by key experiments for guidance and validation at UC Berkeley-BioE.

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Section: Discussionmentioning

confidence: 99%

Section: Technical Approachmentioning

confidence: 99%

FY05 LDRD Final Report A Computational Design Tool for Microdevices and Components in Pathogen Detection Systems

Trebotich¹

2006

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“…The embedded boundary approach to complex geometry is compatible with a fast and accurate technique for surface extraction from CAD and image data [2,9]. In this technique fast marching level sets methods are used to obtain a surface rendering from the image.…”

Section: Embedded Boundary / Volume-of-fluid Methodsmentioning

confidence: 99%

Simulation of biological flow and transport in complex geometries using embedded boundary/volume-of-fluid methods

Trebotich

2007

J. Phys.: Conf. Ser.

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Abstract. We have developed a simulation capability to model multiscale flow and transport in complex biological systems based on algorithms and software infrastructure developed under the SciDAC APDEC CET. The foundation of this work is a new hybrid fluid-particle method for modeling polymer fluids in irregular microscale geometries that enables long-time simulation. Both continuum viscoelastic and discrete particle representations have been used to model the constitutive behavior of polymer fluids. Flow environment geometries are represented on Cartesian grids using an implicit function. Direct simulation of flow in the irregular geometry is then possible using embedded boundary / volume-of-fluid methods without loss of geometric detail. This capability has been used to simulate biological flows in a variety of application geometries including biomedical microdevices, anatomical structures and porous media. IntroductionBiological flow is complex and not well-understood due to the presence of macromolecules in the fluid whose molecular lengths are comparable to the flow geometries of microscale systems. This is inherently a multi-scale problem as flow, transport, molecular recognition, and reaction in these types of flows require description of multi-species processes in complex three-dimensional geometries. For example, in a biomedical microdevice the length scales range from about 1 nanometer (the scale at which the surface of a small globular protein is discretized), to tens or hundreds of microns for typical fluidic processors. The time scales range from tens of nanoseconds (a characteristic time for protein reorientation) to a few minutes (a typical time for a complete bioassay). Currently it is not possible to computationally resolve the phenomena of interest over this range of length and time scales in a computational design or modeling tool. Additionally, available software packages do not contain advanced numerical algorithms to physically model such complex fluids.Additional complexity arises from the need to represent realistic geometries in biological flows. Geometric details in device or anatomical flow domains can have an effect at the system level. It is necessary to work within a self-consistent framework that includes capabilities, from surface extraction of image data to direct simulation in the geometry obtained from image data without loss in geometric details. Methods of surface extraction are limited in their ability to recover the geometry of complex objects in real time. Moreover, it is necessary to convert the surface extracted by image processing techniques into a computational domain appropriate for

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“…Excellent work by Wang et al, (Wang et al, 2004) show the power of evolving a curve to map prominent structures in an image. Deschamps et al (Deschamps et al, 2004) used level sets combined with embedded boundary methods to simulate blood flow and segment major vessels. Lowell, et al (Lowell et al, 2004) used active contours, the fore-runner to level sets, to find the optic nerve head.…”

Section: Segmentation Algorithmsmentioning

confidence: 99%

Lesion Boundary Segmentation Using Level Set Methods

Massey

Lowell²,

Hunter

et al. 2009

Proceedings of the Fourth International Conference on Computer Vision Theory and Applications

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Abstract:This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism -an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, 'looks back in time' to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician marked-up boundaries as ground truth.

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Vessel segmentation and blood flow simulation using Level-Sets and Embedded Boundary methods

Cited by 39 publications

References 7 publications

FY05 LDRD Final Report A Computational Design Tool for Microdevices and Components in Pathogen Detection Systems

FY05 LDRD Final Report A Computational Design Tool for Microdevices and Components in Pathogen Detection Systems

Simulation of biological flow and transport in complex geometries using embedded boundary/volume-of-fluid methods

Lesion Boundary Segmentation Using Level Set Methods

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