Multiscale Vessel Tracking

Wink, Onno; Niessen, Wiro J.; Viergever, Max A.

doi:10.1109/tmi.2003.819920

Cited by 176 publications

(116 citation statements)

References 7 publications

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“…The attributed graph is formalized using the Graph eXchange Language (GXL) 4 , an open standard exchange format for graphs [25], adhering to the eXtensible Markup Language (XML) and constrained by a Document Type Definition (DTD). Our implementation is also capable of exporting the tree in the DOT language 5 , a plain text graph description language, which can be imported into different software. We use the Graph Visualization software, Graphviz 6 , to display the resulting graphs.…”

Section: Graph-based Modeling Of the Vasculaturementioning

confidence: 99%

“…This strong need has been the catalyst for algorithms for segmentation and analysis of vasculature. This has been reflected by the large number of proposed algorithms for enhancement, segmentation, and analysis of vasculature and perfusion data from 3D medical images in the past few decades [3,4,5,6,7,8,9,10,11,12]. However, what has been lacking is the existence of benchmarks for the validation, evaluation, and comparison of all these approaches.…”

Section: Introductionmentioning

confidence: 99%

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VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis

Hamarneh

Jassi

2010

Computerized Medical Imaging and Graphics

134

125

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Automated segmentation and analysis of tree-like structures from 3D medical images are important for many medical applications, such as those dealing with blood vasculature or lung airways. However, there is an absence of large databases of expert segmentations and analyses of such 3D medical images, which impedes the validation and training of proposed image analysis algorithms. In this work, we simulate volumetric images of vascular trees and generate the corresponding ground truth segmentations, bifurcation locations, branch properties, and tree hierarchy. The tree generation is performed by iteratively growing a vascular structure based on a user-defined (possibly spatially varying) oxygen demand map. We describe the details of the algorithm and provide a variety of example results.

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Section: Graph-based Modeling Of the Vasculaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis

Hamarneh

Jassi

2010

Computerized Medical Imaging and Graphics

134

125

View full text Add to dashboard Cite

show abstract

“…Unfortunately, the computational times were unrealistic in all those reported strategies. As an alternative, numerous studies have proposed extraction of the vessel structures only from angiographic images [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. In those studies, the centerlines were tracked or vessel regions were segmented from angiographic images.…”

Section: Introductionmentioning

confidence: 99%

Respiratory-synchronized digital subtraction angiography based on a respiratory phase matching method

Ohnishi

Takano

Kato

et al. 2017

SIViP

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Catheter-based therapy is typically performed under fluoroscopic image observation. However, the vessel structure cannot be visualized on fluoroscopic images. To overcome this limitation, angiographic images are captured with a contrast agent, and digital subtraction angiography (DSA) images are acquired during the intervention. In thoracoabdominal DSA, patients have to hold their breath to match the respiratory phase during DSA acquisition. However, breath holding is difficult for some patients, including the elderly. If the organs move during DSA acquisition, artifacts can occur on DSA images, and the DSA acquisition must be performed again. In the present study, we describe and characterize a new respiratory phase matching method for respiratory-synchronized DSA acquisition under natural respiration. Preoperative angiographic and intra-operative fluoroscopic images were collected under natural respiration during the operation. For each fluoroscopic image, we used a pattern matching to select an angiographic image in the most similar respiratory phase. We then examined whether the method could be applied to both the free breathing DSA and the respiratory-synchronized roadmap. We found that our proposed respiratory phase matching method produced acceptable DSA images without breath holding, and that the processing could be performed in real time. B Takashi Ohnishi

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“…In most cases, methods rely on the detection of local tubular structures based on the local intensity characteristics. The feature extraction process in 3D images can be performed by tracking the vessel centerlines [5,6] or by extracting the vessel wall [7]. While most centerline-based methods directly estimate vessel diameters, they may have limitations such as their inability to track more than one branch at a time.…”

Section: Introductionmentioning

confidence: 99%

Vessel Centerline Tracking in CTA and MRA Images Using Hough Transform

Macedo

Mekkaoui

Jackowski

2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Vascular disease is characterized by any condition that affects the circulatory system. Recently, a demand for sophisticated software tools that can characterize the integrity and functional state of vascular networks from different vascular imaging modalities has appeared. Such tools face significant challenges such as: large datasets, similarity in intensity distributions of other organs and structures, and the presence of complex vessel geometry and branching patterns. Towards that goal, this paper presents a new approach to automatically track vascular networks from CTA and MRA images. Our methodology is based on the Hough transform to dynamically estimate the centerline and vessel diameter along the vessel trajectory. Furthermore, the vessel architecture and orientation is determined by the analysis of the Hessian matrix of the CTA or MRA intensity distribution. Results are shown using both synthetic vessel datasets and real human CTA and MRA images. The tracking algorithm yielded high reproducibility rates, robustness to different noise levels, associated with simplicity of execution, which demonstrates the feasibility of our approach.

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Multiscale Vessel Tracking

Cited by 176 publications

References 7 publications

VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis

VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis

Respiratory-synchronized digital subtraction angiography based on a respiratory phase matching method

Vessel Centerline Tracking in CTA and MRA Images Using Hough Transform

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