Vascular Centerline Extraction in 3D MR Angiograms for Phase Contrast MRI Blood Flow Measurement

Hoyos, Marcela Hernández; Orłowski, Piotr; Piątkowska-Janko, Ewa; Bogorodzki, Piotr; Orkisz, Maciej

doi:10.1007/s11548-006-0005-0

Cited by 19 publications

(9 citation statements)

References 26 publications

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“…However, it should be used cautiously and not as the only indicator for rupture risk assessment. Other geometric indices such as wall thickness [14,21], surface curvatures [13,14], volume and surface area [14], thrombus volume [15], etc., should be accounted for to increase the accuracy of this estimation. Fillinger et al [29] also report on the influence of geometric variables in the classification of ruptured and unruptured AAAs.…”

Section: Discussionmentioning

confidence: 99%

“…This method requires two assumptions that are not always valid: (i) that the intensities along the centerline follow a Gaussian distribution and (ii) that they follow the same (Gaussian) distribution. Conversely, Hoyos et al [15] applied moments of inertia to predict the projection of the centerline iteratively. The results are superior on cases with adequate contrast mixing and small data sizes, but the method has a similar disadvantage to the Hessian matrix based solution.…”

Section: Introductionmentioning

confidence: 99%

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Robust infrarenal aortic aneurysm lumen centerline detection for rupture status classification

Zhang

Kheyfets

Finol

2013

Medical Engineering & Physics

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The objective of this work is to develop a robust method for human abdominal aortic aneurysm (AAA) centerline detection that can contribute to the accurate computation of features for the prediction of AAA rupture risk. A semiautomatic algorithm is proposed for detecting the lumen centerline in contrast-enhanced abdominal computed tomography images based on online adaboost classifiers, which does not require prior image segmentation. The algorithm was developed and applied to thirty ruptured and thirty unruptured AAA image data sets and the tortuosities of the detected centerline were measured to assess the correlation between AAA tortuosity and the binary ruptured and unruptured labels. The lumen of each data set was segmented manually by a trained radiologist and the resulting centerlines of each data set were defined as the gold standard to evaluate the accuracy of the algorithm and to compare it against two widely used segmentation techniques. The average mean relative accuracy of the offline adaboost classifier is 91.9% with a standard deviation of 1.6%; for the online adaboost classifier it is 93.6% with a standard deviation of 1.9% (p<0.05). The online adaboost classifier outperforms the offline adaboost classifier while their computational costs are similar. Aneurysm tortuosity computed from an accurately derived lumen centerline using online adaboost is statistically higher for ruptured aneurysms compared to unruptured aneurysms, indicating that tortuosity can be used to assess rupture risk in the vascular clinic.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust infrarenal aortic aneurysm lumen centerline detection for rupture status classification

Zhang

Kheyfets

Finol

2013

Medical Engineering & Physics

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“…This method requires two assumptions that are not always valid: one is that the intensities along the centerline follow a Gaussian distribution and the other is that they follow the same distribution. Hoyos et al [14] applied moments of inertia to predict the projection of the centerline iteratively. The results are good on cases with good contrast and small sizes.…”

Section: Discussionmentioning

confidence: 99%

Robust and fast abdominal aortic aneurysm centerline detection for rupture risk prediction

Zhang

Finol

2011

SPIE Proceedings

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This work describes a robust and fast semi-automatic approach for Abdominal Aortic Aneurysm (AAA) centerline detection. AAA is a vascular disease accompanied by progressive enlargement of the abdominal aorta, which leads to rupture if left untreated, an event that accounts for the 13th leading cause of death in the U.S. The lumen centerline can be used to provide the initial starting points for thrombus segmentation. Different from other methods, which are mostly based on region growing and suffer from problems of leakage and heavy computational burden, we propose a novel method based on online classification. An online version of the adaboost classifier based on steerable features is applied to AAA MRI data sets with a rectangular box enclosing the lumen in the first slice. The classifier is updated during the tracking process by using the testing result of the previous image as the new training data. Unlike traditional offline versions, the online classifier can adjust parameters automatically when a leakage occurs. With the help of integral images on the computation of haar-like features, the method can achieve nearly real time processing (about 2 seconds per image on a standard workstation). Ten ruptured and ten unruptured AAA data sets were processed and the tortuosity of the 20 centerlines was calculated. The correlation coefficient of the tortuosity was calculated to illustrate the significance of the prediction with the proposed method. The mean relative accuracy is 95.68% with a standard deviation of 0.89% when compared to a manual segmentation procedure. The correlation coefficient is 0.394.

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“…A volume of interest (VOI) and a point within the vessel are first interactively determined. The vessel central axis is then automatically extracted by an extensible-skeleton method [12,13]. Vessel boundaries are subsequently detected in planes locally orthogonal to the centerline, using an adaptive iso-contour method.…”

Section: Methodsmentioning

confidence: 99%

“…The 50% iso-value was also used in CE-MRA [5,6,18]. However, our previous experience [13] showed that an iso-value of 45% leads to better results in CE-MRA images. This value was fixed during all the experimentation.…”

Section: Vessel Boundary Detection Via Adaptive Iso-contoursmentioning

confidence: 99%

Evaluation of semi-automatic arterial stenosis quantification

et al. 2006

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Object: To assess the accuracy and reproducibility of semi-automatic vessel axis extraction and stenosis quantification in 3D contrast-enhanced Magnetic Resonance Angiography (CE-MRA) of the carotid arteries (CA). Materials and methods: A total of 25 MRA datasets was used: 5 phantoms with known stenoses, and 20 patients (40 CAs) drawn from a multicenter trial database. Maracas software extracted vessel centerlines and quantified the stenoses, based on boundary detection in planes perpendicular to the centerline. Centerline accuracy was visually scored. Semi-automatic measurements were compared with: (1) theoretical phantom morphometric values, and (2) stenosis degrees evaluated by two independent radiologists. Results: Exploitable centerlines were obtained in 97% of CA and in all phantoms. In phantoms, the software achieved a better agreement with theoretic stenosis degrees (weighted kappa κ w = 0.91) than the radiologists (κ w = 0.69). In patients, agreement between software and radiologists varied from κ w =0.67 to 0.90. In both, Maracas was substantially more reproducible than the readers. Mean operating time was within 1 min/ CA. Conclusion: Maracas software generates accurate 3D centerlines of vascular segments with minimum user intervention. Semi-automatic quantification of CA stenosis is also accurate, except in very severe stenoses that cannot be segmented. It substantially reduces the inter-observer variability.

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Vascular Centerline Extraction in 3D MR Angiograms for Phase Contrast MRI Blood Flow Measurement

Cited by 19 publications

References 26 publications

Robust infrarenal aortic aneurysm lumen centerline detection for rupture status classification

Robust infrarenal aortic aneurysm lumen centerline detection for rupture status classification

Robust and fast abdominal aortic aneurysm centerline detection for rupture risk prediction

Evaluation of semi-automatic arterial stenosis quantification

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