A patient-specific coronary density estimate

Shahzad, Rahil; Schaap, Michiel; Walsum, Theo van; Klien, Stephanie; Weustink, Annick C.; Vliet, Lucas J. van; Niessen, Wiro J.

doi:10.1109/isbi.2010.5490426

Cited by 10 publications

(13 citation statements)

References 10 publications

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“…The proposed method can be extended to define an ROI mesh for the whole left or right coronary tree (including all side branches). In [11], a similar ROI is exploited for automatic calcium scoring. However, their ROI is generated by multi-atlas based registration, which is far more time consuming than our approach.…”

Section: Building Prior Coronary Modelmentioning

confidence: 99%

Robust and Accurate Coronary Artery Centerline Extraction in CTA by Combining Model-Driven and Data-Driven Approaches

Zheng

Tek

Funka-Lea

2013

Advanced Information Systems Engineering

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Abstract. Various methods have been proposed to extract coronary artery centerlines from computed tomography angiography (CTA) data. Almost all previous approaches are data-driven, which try to trace a centerline from an automatically detected or manually specified coronary ostium. No or little high level prior information is used; therefore, the centerline tracing procedure may terminate early at a severe occlusion or an anatomically inconsistent centerline course may be generated. Though the connectivity of coronary arteries exhibits large variations, the position of major coronary arteries relative to the heart chambers is quite stable. In this work, we propose to exploit the automatically segmented chambers to 1) predict the initial position of the major coronary centerlines and 2) define a vessel-specific region-of-interest (ROI) to constrain the following centerline refinement. The proposed prior constraints have been integrated into a model-driven algorithm for the extraction of three major coronary centerlines, namely the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA). After extracting the major coronary arteries, the side branches are traced using a data-driven approach to handle large anatomical variations in side branches. Experiments on the public Rotterdam coronary CTA database demonstrate the robustness and accuracy of the proposed method. We achieve the best average ranking on overlap metrics among automatic methods and our accuracy metric outperforms all other 22 methods (including both automatic and semi-automatic methods).

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Section: Building Prior Coronary Modelmentioning

confidence: 99%

Robust and Accurate Coronary Artery Centerline Extraction in CTA by Combining Model-Driven and Data-Driven Approaches

Zheng

Tek

Funka-Lea

2013

Advanced Information Systems Engineering

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“…A result for cerebral arteries, obtained from 9 patients is depicted in Figure 5.4. Nonetheless, for creating vascular atlases as density fields, a recent strategy has been proposed in [88], for cardiovascular CT data. By opposition to the case of cerebral MRA data, which requires the simultaneous use of MRI data, here, the considered CTA images contained Figure 5.5: 3D visualisation of the vascular atlas (here, thresholded density field) for the three main coronary arteries (centerlines of which, for a given CTA, are depicted in green, red and yellow).…”

Section: Recent Workmentioning

confidence: 99%

“…By opposition to the case of cerebral MRA data, which requires the simultaneous use of MRI data, here, the considered CTA images contained Figure 5.5: 3D visualisation of the vascular atlas (here, thresholded density field) for the three main coronary arteries (centerlines of which, for a given CTA, are depicted in green, red and yellow). Illustration from [88], ( c �2010 IEEE).…”

Section: Recent Workmentioning

confidence: 99%

“…An approach described in [77] proposes such a method. It requires as input more information than a simple segmentation, namely a segmented volume (as in [69]), the associated medial axes (as in [16,20,88]), but also information on the vessel orientation (it has to be noticed that all these information elements may be obtained from a segmented volume, by using adequate methods 11 ). It also requires correct deformation fields in order to register these different data with an anatomical reference.…”

Section: Recent Workmentioning

confidence: 99%

“…Nonrigid registration of these morphological images then provides the deformation fields enabling to match the segmentation, medial axes and orientation maps associated to each image onto the anatomical reference. The mean and variance values for each one of these scalar and vectorial attributes finally lead to a vascular density field (as in [16,20,88]), but also to size and orientation intervals at each vascular point of the atlas. Such an atlas, modelling both cerebral veins and arteries, built from 16 patients is partially illustrated in Figure 5.6.…”

Section: Recent Workmentioning

confidence: 99%

See 2 more Smart Citations

Angiographic Image Analysis

Tankyevych

Talbot

Passat

et al. 2011

Medical Image Processing

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Automatic segmentation, detection and quantification of coronary artery stenoses on CTA

Shahzad

Kirişli

Metz

et al. 2013

Int J Cardiovasc Imaging

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Accurate detection and quantification of coronary artery stenoses is an essential requirement for treatment planning of patients with suspected coronary artery disease. We present a method to automatically detect and quantify coronary artery stenoses in computed tomography coronary angiography. First, centerlines are extracted using a two-point minimum cost path approach and a subsequent refinement step. The resulting centerlines are used as an initialization for lumen segmentation, performed using graph cuts. Then, the expected diameter of the healthy lumen is estimated by applying robust kernel regression to the coronary artery lumen diameter profile. Finally, stenoses are detected and quantified by computing the difference between estimated and expected diameter profiles. We evaluated our method using the data provided in the Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Using 30 testing datasets, the method achieved a detection sensitivity of 29% and a positive predictive value (PPV) of 24% as compared to quantitative coronary angiography (QCA), and a sensitivity of 21% and a PPV of 23% as compared manual assessment based on consensus reading of CTA by 3 observers. The stenoses degree was estimated with an absolute average difference of 31%, a root mean square difference of 39.3% when compared to QCA, and a weighted kappa value of 0.29 when compared to CTA. A Dice of 68 and 65% was reported for lumen segmentation of healthy and diseased vessel segments respectively. According to the ranking of the evaluation framework, our method finished fourth for stenosis detection, second for stenosis quantification and second for lumen segmentation.

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A patient-specific coronary density estimate

Cited by 10 publications

References 10 publications

Robust and Accurate Coronary Artery Centerline Extraction in CTA by Combining Model-Driven and Data-Driven Approaches

Robust and Accurate Coronary Artery Centerline Extraction in CTA by Combining Model-Driven and Data-Driven Approaches

Angiographic Image Analysis

Automatic segmentation, detection and quantification of coronary artery stenoses on CTA

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