Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions

Kurugol, Sila; Come, Carolyn E.; Díaz, Alejandro A.; Ross, James C.; Kinney, Gregory L.; Black-Shinn, Jennifer L.; Hokanson, John E.; Budoff, Matthew J.; Washko, George R.; Estépar, Raúl San Jośe

doi:10.1118/1.4924500

Cited by 31 publications

(19 citation statements)

References 29 publications

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“…no. ]Evaluation data sizeDSCJaccard coefficientMSD (mm)Kitasaka et al [15]7 CT0.93 ± 0.03–0.90 ± 0.33Avila-Montes et al [16]45 CT0.84 ± 0.100.74 ± 0.13–Kurugol et al [17]45 CT0.92 ± 0.010.85 ± 0.020.62 ± 0.09Isgum et al [18]29 CT–0.78 ± 0.04–Xie et al [19]60 CT0.93 ± 0.01–1.39 ± 0.19Proposed method100 CT0.95 ± 0.010.90 ± 0.010.56 ± 0.08 DSC dice similarity coefficient, MSD mean surface distance…”

Section: Discussionmentioning

confidence: 99%

Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT

et al. 2019

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Objectives To develop and evaluate a fully automatic method to measure diameters of the ascending and descending aorta on non-ECG-gated, non-contrast computed tomography (CT) scans. Material and methods The method combines multi-atlas registration to obtain seed points, aorta centerline extraction, and an optimal surface segmentation approach to extract the aorta surface around the centerline. From the extracted 3D aorta segmentation, the diameter of the ascending and descending aorta was calculated at cross-sectional slices perpendicular to the extracted centerline, at the level of the pulmonary artery bifurcation, and at 1-cm intervals up to 3 cm above and below this level. Agreement with manual annotations was evaluated by dice similarity coefficient (DSC) for segmentation overlap, mean surface distance (MSD), and intra-class correlation (ICC) of diameters on 100 CT scans from a lung cancer screening trial. Repeatability of the diameter measurements was evaluated on 617 baseline-one year follow-up CT scan pairs. Results The agreement between manual and automatic segmentations was good with 0.95 ± 0.01 DSC and 0.56 ± 0.08 mm MSD. ICC between the diameters derived from manual and from automatic segmentations was 0.97, with the per-level ICC ranging from 0.87 to 0.94. An ICC of 0.98 for all measurements and per-level ICC ranging from 0.91 to 0.96 were obtained for repeatability. Conclusion This fully automatic method can assess diameters in the thoracic aorta reliably even in non-ECG-gated, non-contrast CT scans. This could be a promising tool to assess aorta dilatation in screening and in clinical practice. Key Points • Fully automatic method to assess thoracic aorta diameters. • High agreement between fully automatic method and manual segmentations. • Method is suitable for non-ECG-gated CT and can therefore be used in screening. Electronic supplementary material The online version of this article (10.1007/s00330-018-5931-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT

et al. 2019

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“…Secondly, the curvature angle determination method adopted is simplified and only reflects the global pattern of the aortic configuration; i.e., it does not capture local variations in curvature. Hence, detailed image analysis approaches 19 , 21 , 24 are necessary to more precisely correlate the aortic morphology with clot trajectory and stroke propensity. This also makes it more plausible to examine how altered cardiac hemodynamics is related to the flow field including pressure distribution in each aortic branch relative to aorta.…”

Section: Discussionmentioning

confidence: 99%

Role of Aortic Geometry on Stroke Propensity based on Simulations of Patient-Specific Models

Choi

Luo

Navia

et al. 2017

Sci Rep

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Stroke is a life threatening event that is expected to more than double over the next 40 years. Atrial fibrillation (AF) has been reported as a strong independent risk factor for stroke. We have previously shown that a hemodynamic perturbation by AF or reduced cardiac output and cycle length may have a significant impact on clot trajectory and thus embolic stroke propensity through the left common carotid artery using an idealized aortic arch model. Here, we show the dependence of flow patterns and hence stroke propensity on geometry of patient-specific aortas. We performed computational fluid dynamics (CFD) simulations to determine the variations of AF-induced stroke propensity over various image-based patient-dependent aorta models. The results demonstrated that curvature pattern of aorta can play a determinant role in AF-induced stroke propensity alteration. Specifically, it was shown that the hemodynamic perturbation by AF considered led to substantial increase in stroke propensity (i.e., 2.5~3.8 fold elevation) for lower curvature angle <90° while the changes in stroke propensity by AF are negligible for higher curvature angle >90°. The present simulations suggest that aortic arch curvature is an important risk factor for embolic stroke which should be tested in future clinical trials.

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“…Algorithms for aortic wall segmentation can be divided into three categories based on region (thoracic/abdomen), disease (aneurysm/non-aneurysm) and imaging modality. In previous studies, several segmentation algorithms [7][8][9][10][11] were developed for thoracic aorta without aneurysm in non-contrast and contrast enhanced CT. Xie et al [7] presented an algorithm using anatomical location and cylindertracking in non-contrast enhanced CT images of the thoracic aorta. Isgam et al [8] proposed multi-atlas-based segmentation for non-contrast CT images of thoracic aorta.…”

Section: Related Workmentioning

confidence: 99%

“…Isgam et al [8] proposed multi-atlas-based segmentation for non-contrast CT images of thoracic aorta. Kurugol et al [9,10] proposed an algorithm to detect the outer wall contour of non-contrast enhanced CT images of thoracic aorta, using anatomical location and circular Hough transform followed by 3D level set segmentation. Raman et al [11] developed an algorithm to define the outer wall contour of thoracic and abdominal aorta without aneurysm on contrast enhanced CT images by using a minimum cost path through the graph constructed of the pixels.…”

Section: Related Workmentioning

confidence: 99%

Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces

2018

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Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.

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Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions

Abstract: The authors have developed an objective tool to assess aorta morphology and aortic calcium plaques on CT scans that may be used to provide information about the presence of cardiovascular disease and its clinical impact in smokers.

Cited by 31 publications

References 29 publications

Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT

Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT

Role of Aortic Geometry on Stroke Propensity based on Simulations of Patient-Specific Models

Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces

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