Graph-Assisted Visualization of Microvascular Networks

Govyadinov, Pavel A.; Womack, Tasha; Eriksen, Jason L.; Mayerich, David; Chen, Guoning

doi:10.1109/visual.2019.8933682

Cited by 6 publications

(7 citation statements)

References 32 publications

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“…47 This method provides whole organ images at high contrast, making them easier to reconstruct. Vessel center lines and radii were extracted using an automated predictor-corrector algorithm 49 that reconstructs the medial axis of each capillary fiber. This created an explicit graph model storing the architecture of the segmented network (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…48 A 652 × 652 × 100 pixel (120 × 120 × 100 mm) region of interest (ROI) was identified and extracted from the whole-brain data set. Microvessel centerlines and connectivity were segmented using a predictor–corrector algorithm, 49 while the surface model and radii were extracted manually by setting a threshold to separate the microvascular structure from the background. This data was combined to generate a graph-based model used as input to the AFAN software.…”

Section: Methodsmentioning

confidence: 99%

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Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow

et al. 2019

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow

et al. 2019

Self Cite

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“…Techniques like hatching can be used to identify certain regions. Fifth, if multiple attributes must be visualized, glyphs can be used as an additional way to encode information [MMNG16, GWE * 19a]. Symbols that encode attributes through their shape, size, and color are common in traditional maps, as they can add explorable layers of information to a spatial domain without the need for direct interaction.…”

Section: Taxonomymentioning

confidence: 99%

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

Eulzer

Meuschke²,

Mistelbauer

et al. 2022

Computer Graphics Forum

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Input Graph LayoutFigure 1: We describe the creation of a vessel map as a three-step process: the layout of the map must be derived, the geometry or spatiality of the data must be mapped, and the data attributes must be mapped. We differentiate between image volume maps, flow volume maps, vessel wall maps, and vessel network maps depending on the primary data structure the map-like visualization is based on.

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“…Govyadinov et al [14] described a template-based predictor-corrector method for tracing filaments that is robust in microvascular datasets, and applied a number of glyph-based visualization techniques to represent the aggregated and biologically relevant information of the extracted microvascular network. Then, they developed a bi-modal visualization framework [15], leveraging graph-based and geometry-based techniques to achieve interactive visualization of microvascular networks. However, these approaches are exhausted by handcrafted features (e.g., gradients of the intensity, second order local structures, maximum principal curvatures) and complicated manual parameter adjustment to adapt to the subject variations.…”

Section: Model-driven Vessel Extraction and Segmentationmentioning

confidence: 99%

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data

Wang

Yan

Zhu

et al. 2020

Preprint

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The fundamental motivation of the proposed work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration. For example, extracting and visualizing microstructures in-vivo have been a long-standing challenging problem. However, due to the high sparseness and noisiness in cerebrovasculature data as well as highly complex geometry and topology variations of micro vessels, it is still extremely challenging to extract the complete 3D vessel structure and visualize it in 3D with high fidelity. In this paper, we present an end-to-end deep learning method, VC-Net, for robust extraction of 3D microvascular structure through embedding the image composition, generated by maximum intensity projection (MIP), into the 3D volumetric image learning process to enhance the overall performance. The core novelty is to automatically leverage the volume visualization technique (e.g., MIP -a volume rendering scheme for 3D volume images) to enhance the 3D data exploration at the deep learning level. The MIP embedding features can enhance the local vessel signal (through canceling out the noise) and adapt to the geometric variability and scalability of vessels, which is of great importance in microvascular tracking. A multi-stream convolutional neural network (CNN) framework is proposed to effectively learn the 3D volume and 2D MIP feature vectors, respectively, and then explore their inter-dependencies in a joint volume-composition embedding space by unprojecting the 2D feature vectors into the 3D volume embedding space. It is noted that the proposed framework can better capture the small / micro vessels and improve the vessel connectivity. To our knowledge, this is the first time that a deep learning framework is proposed to construct a joint convolutional embedding space, where the computed vessel probabilities from volume rendering based 2D projection and 3D volume can be explored and integrated synergistically. Experimental results are evaluated and compared with the traditional 3D vessel segmentation methods and the state-of-the-art in deep learning, by using extensive public and real patient (micro-)cerebrovascular image datasets. The application of this accurate segmentation and visualization of sparse and complicated 3D microvascular structure facilitated by our method demonstrates the potential in a powerful MR arteriogram and venogram diagnosis of vascular disease.

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Graph-Assisted Visualization of Microvascular Networks

Cited by 6 publications

References 32 publications

Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow

Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data

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