Morphological Geodesic Active Contour Based Automatic Aorta Segmentation in Thoracic CT Images

Dasgupta, Avijit; Mukhopadhyay, Sudipta; Mehre, Shrikant A.; Bhattacharyya, Parthasarathi

doi:10.1007/978-981-10-2107-7_17

Cited by 6 publications

(10 citation statements)

References 15 publications

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“…Thus, there is a need to develop an inspection algorithm to identify defective 3D film products. Quality inspection for 3D films can be performed using existing research methods [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

et al. 2023

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A 3D film pattern image was recently developed for marketing purposes, and an inspection method is needed to evaluate the quality of the pattern for mass production. However, due to its recent development, there are limited methods to inspect the 3D film pattern. The good pattern in the 3D film has a clear outline and high contrast, while the bad pattern has a blurry outline and low contrast. Due to these characteristics, it is challenging to examine the quality of the 3D film pattern. In this paper, we propose a simple algorithm that classifies the 3D film pattern as either good or bad by using the height of the histograms. Despite its simplicity, the proposed method can accurately and quickly inspect the 3D film pattern. In the experimental results, the proposed method achieved 99.09% classification accuracy with a computation time of 6.64 s, demonstrating better performance than existing algorithms.

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Section: Introductionmentioning

confidence: 99%

“…Recently, segmentation methods, such as morphological geodesic active contour methods, have been published [ 5 , 6 , 7 ]. Morphological geodesic active contour gradually develops segmentation areas and then morphologically segments the objects.…”

Section: Introductionmentioning

confidence: 99%

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

et al. 2023

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“…Despite the widespread use of noncontrast CT in clinical practice, relatively fewer studies can be found on these scans, on the segmentation of the aorta [19][20][21][22][23][24][25][26][27][28][29][30][31] and especially the PA containing the pulmonary trunk, left and right pulmonary arteries. 30,32,33 Among existing segmentation methods, those using a shape prior 20,27,28,33 generally obtain good segmentation results on noncontrast CT scans.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the widespread use of noncontrast CT in clinical practice, relatively fewer studies can be found on these scans, on the segmentation of the aorta 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 and especially the PA containing the pulmonary trunk, left and right pulmonary arteries. 30 , 32 , 33 …”

Section: Introductionmentioning

confidence: 99%

Assessment of fully automatic segmentation of pulmonary artery and aorta on noncontrast CT with optimal surface graph cuts

et al. 2021

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Purpose Accurate segmentation of the pulmonary arteries and aorta is important due to the association of the diameter and the shape of these vessels with several cardiovascular diseases and with the risk of exacerbations and death in patients with chronic obstructive pulmonary disease. We propose a fully automatic method based on an optimal surface graph‐cut algorithm to quantify the full 3D shape and the diameters of the pulmonary arteries and aorta in noncontrast computed tomography (CT) scans. Methods The proposed algorithm first extracts seed points in the right and left pulmonary arteries, the pulmonary trunk, and the ascending and descending aorta by using multi‐atlas registration. Subsequently, the centerlines of the pulmonary arteries and aorta are extracted by a minimum cost path tracking between the extracted seed points, with a cost based on a combination of lumen intensity similarity and multiscale medialness in three planes. The centerlines are refined by applying the path tracking algorithm to curved multiplanar reformatted scans and are then smoothed and dilated nonuniformly according to the extracted local vessel radius from the medialness filter. The resulting coarse estimates of the vessels are used as initialization for a graph‐cut segmentation. Once the vessels are segmented, the diameters of the pulmonary artery (PA) and the ascending aorta (AA) and the PA:AA ratio are automatically calculated both in a single axial slice and in a 10 mm volume around the automatically extracted PA bifurcation level. The method is evaluated on noncontrast CT scans from the Danish Lung Cancer Screening Trial (DLCST). Segmentation accuracy is determined by comparing with manual annotations on 25 CT scans. Intraclass correlation (ICC) between manual and automatic diameters, both measured in axial slices at the PA bifurcation level, is computed on an additional 200 CT scans. Repeatability of the automated 3D volumetric diameter and PA:AA ratio calculations (perpendicular to the vessel axis) are evaluated on 118 scan–rescan pairs with an average in‐between time of 3 months. Results We obtained a Dice segmentation overlap of 0.94 ± 0.02 for pulmonary arteries and 0.96 ± 0.01 for the aorta, with a mean surface distance of 0.62 ± 0.33 mm and 0.43 ± 0.07 mm, respectively. ICC between manual and automatic in‐slice diameter measures was 0.92 for PA, 0.97 for AA, and 0.90 for the PA:AA ratio, and for automatic diameters in 3D volumes around the PA bifurcation level between scan and rescan was 0.89, 0.95, and 0.86, respectively. Conclusion The proposed automatic segmentation method can reliably extract diameters of the large arteries in non‐ECG‐gated noncontrast CT scans such as are acquired in lung cancer screening.

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“…This is the case for Kurugol et al [6], where ascending and descending aorta were modeled as tubular shapes and the arch section as a torus, but in any case, preserving the circular shape for cross sections. Dasgupta et al [7] proposed the use of morphological geodesic active contours to segment the aorta. The aorta boundary for both ascending and descending aorta was located by using a circular Hough Transform.…”

Section: Introductionmentioning

confidence: 99%

Automatic estimation of the aortic lumen geometry by ellipse tracking

et al. 2018

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Purpose The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algorithm to extract the aorta geometry for either normal (with and without contrast) or abnormal computed tomography (CT) cases. Methods The algorithm we propose is a fast incremental technique that computes the 3D geometry of the aortic lumen from an initial contour located inside it. Our approach is based on the optimization of the 3D orientation of the cross sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. The optimization involves the size and eccentricity of the ellipse which best fits the aorta contour on each cross-sectional plane. The method works directly on the original CT and does not require a prior segmentation of the aortic lumen. We present experimental results to show the accuracy of the method and its ability to cope with challenging CT cases where the aortic lumen may have low contrast, different kinds of pathologies, artifacts, and even significant angulations due to severe elongations. Results The algorithm correctly tracked the aorta geometry in 380 of 385 CT cases. The mean of the dice similarity coefficient was 0.951 for aorta cross sections that were randomly selected from the whole database. The mean distance to a manually delineated segmentation of the aortic lumen was 0.9 mm for sixteen selected cases. Conclusions The results achieved after the evaluation demonstrate that the proposed algorithm is robust and accurate for the automatic extraction of the aorta geometry for both normal (with and without contrast) and abnormal CT volumes.

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Morphological Geodesic Active Contour Based Automatic Aorta Segmentation in Thoracic CT Images

Cited by 6 publications

References 15 publications

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

Assessment of fully automatic segmentation of pulmonary artery and aorta on noncontrast CT with optimal surface graph cuts

Automatic estimation of the aortic lumen geometry by ellipse tracking

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