Method for extracting the aorta from 3D CT images

Taeprasartsit, Pinyo; Higgins, William E.

doi:10.1117/12.708522

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…In the first classification stage 22 out of the maximum 25 features were selected. According to the ordering in Table I those were 6, 58, 3,56,15,30,4,2,5,60,10,12,63,25,7,39,61,9,40,14,55, and 34. In the second classification stage 23 out of the maximum 25 features were selected.…”

Section: Resultsmentioning

confidence: 99%

Automated aortic calcium scoring on low‐dose chest computed tomography

et al. 2010

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Section: Resultsmentioning

confidence: 99%

Automated aortic calcium scoring on low‐dose chest computed tomography

et al. 2010

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“…Therefore, a standard 3D segmentation technique based solely on edge or intensity information fails without either a good initialization or integration of a 3D shape model to limit leakage into neighboring vessels with similar Hounsfield intensity values. Most previous work [11][12][13][14] addressed these difficulties with an approach that relies on a priori shape models built from manually segmented training aorta centerlines. However, small training sets cannot represent the anatomical variabilities of aorta shapes and modeling anatomical variabilities across subjects requires a complex 3D shape model.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…To address this difficulty, most algorithms developed for non-contrast CT ([3], [4], [7], [6]) use model based approaches and need a priori models built from manually segmented training data. However, these approaches might fail since they have limited flexibility to capture variabilities.…”

Section: Introductionmentioning

confidence: 99%

Aorta segmentation with a 3D level set approach and quantification of aortic calcifications in non-contrast chest CT

Kurugol

Estépar

Ross

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Automatic aorta segmentation in thoracic computed tomography (CT) scans is important for aortic calcification quantification and to guide the segmentation of other central vessels. We propose an aorta segmentation algorithm consisting of an initial boundary detection step followed by 3D level set segmentation for refinement. Our algorithm exploits aortic cross-sectional circularity: we first detect aorta boundaries with a circular Hough transform on axial slices to detect ascending and descending aorta regions, and we apply the Hough transform on oblique slices to detect the aortic arch. The centers and radii of circles detected by Hough transform are fitted to smooth cubic spline functions using least-squares fitting. From these center and radius spline functions, we reconstruct an initial aorta surface using the Frenet frame. This reconstructed tubular surface is further refined with 3D level set evolutions. The level set framework we employ optimizes a functional that depends on both edge strength and smoothness terms and evolves the surface to the position of nearby edge location corresponding to the aorta wall. After aorta segmentation, we first detect the aortic calcifications with thresholding applied to the segmented aorta region. We then filter out the false positive regions due to nearby high intensity structures. We tested the algorithm on 45 CT scans and obtained a closest point mean error of 0.52 ± 0.10 mm between the manually and automatically segmented surfaces. The true positive detection rate of calcification algorithm was 0.96 over all CT scans.

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Method for extracting the aorta from 3D CT images

Cited by 8 publications

References 10 publications

Automated aortic calcium scoring on low‐dose chest computed tomography

Automated aortic calcium scoring on low‐dose chest computed tomography

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

Aorta segmentation with a 3D level set approach and quantification of aortic calcifications in non-contrast chest CT

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