Automated integer programming based separation of arteries and veins from thoracic CT images

Payer, Christian; Pienn, Michael; Bálint, Zoltán; Shekhovtsov, Alexander; Talakić, Emina; Nagy, Eszter; Olschewski, Andrea; Olschewski, Horst; Urschler, Martin

doi:10.1016/j.media.2016.05.002

Cited by 34 publications

(44 citation statements)

References 39 publications

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“…The proposed method achieves an overall accuracy of 87% when compared to manual reference, with only two cases below 70%. This is lower than the 91.1% accuracy claimed in [8], although a direct comparison cannot be performed as different cases were used for evaluation. The method we propose is similar to [8] in the sense that both methods do not need airway segmentation, but they only require a bronchus enhanced image to exploit the knowledge of proximity of arteries to veins.…”

Section: Discussionmentioning

confidence: 63%

“…Although many methods have been proposed for vessel segmentation [2], only a few studies have attempted to separate AV trees starting from non-contrast CT images [3][4][5][6][7][8][9]. In [3], distances of vessels to bronchi (segmented with a modified region growing method) and to pulmonary fissures (estimated by a Voronoi diagram) are computed and averaged to classify vessel sub-trees.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, two works have been published with the aim of improving available AV segmentation approaches [8,9]. In [9], an algorithm which represents the vessels tree as a graph and uses local information to separate the graph in a set of small sub-tress is presented.…”

Section: Introductionmentioning

confidence: 99%

“…Although this method does not need information about airways, discrimination based only on volume of the tress may be not ideal, especially in patients with specific diseases such as COPD. [8] proposed a fully automatic AV separation algorithm where vessels are classified based on an arterialness measure which indicates their proximity to bronchi. Uniform distribution and reduction of vessel diameter moving towards peripheral areas are also considered.…”

Section: Introductionmentioning

confidence: 99%

“…The neural network is trained to learn information about the vascular trees on small patches surrounding the single vessel candidates defined by the scale-space particles approach described in [13,14]. As in [8], we use a bronchus enhanced image, but we let the net automatically learn the arterialness measure, thus reducing parameters sensitivity. Connectivity information of the single particles, based on their location and strength, is also taken into account.…”

Section: Introductionmentioning

confidence: 99%

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Deep-learning strategy for pulmonary artery-vein classification of non-contrast CT images

Nardelli

Jiménez-Carretero²,

Bermejo-Peláez

et al. 2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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Artery-vein classification on pulmonary computed tomography (CT) images is becoming of high interest in the scientific community due to the prevalence of pulmonary vascular disease that affects arteries and veins through different mechanisms. In this work, we present a novel approach to automatically segment and classify vessels from chest CT images. We use a scale-space particle segmentation to isolate vessels, and combine a convolutional neural network (CNN) to graphcut (GC) to classify the single particles. Information about proximity of arteries to airways is learned by the network by means of a bronchus enhanced image. The methodology is evaluated on the superior and inferior lobes of the right lung of twenty clinical cases. Comparison with manual classification and a Random Forests (RF) classifier is performed. The algorithm achieves an overall accuracy of 87% when compared to manual reference, which is higher than the 73% accuracy achieved by RF.

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Section: Discussionmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep-learning strategy for pulmonary artery-vein classification of non-contrast CT images

Nardelli

Jiménez-Carretero²,

Bermejo-Peláez

et al. 2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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Automatic quantitative analysis of pulmonary vascular morphology in CT images

et al. 2019

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PurposeVascular remodeling is a significant pathological feature of various pulmonary diseases, which may be assessed by quantitative computed tomography (CT) imaging. The purpose of this study was therefore to develop and validate an automatic method for quantifying pulmonary vascular morphology in CT images.MethodsThe proposed method consists of pulmonary vessel extraction and quantification. For extracting pulmonary vessels, a graph‐cuts‐based method is proposed which considers appearance (CT intensity) and shape (vesselness from a Hessian‐based filter) features, and incorporates distance to the airways into the cost function to prevent false detection of airway walls. For quantifying the extracted pulmonary vessels, a radius histogram is generated by counting the occurrence of vessel radii, calculated from a distance transform‐based method. Subsequently, two biomarkers, slope α and intercept β, are calculated by linear regression on the radius histogram. A public data set from the VESSEL12 challenge was used to independently evaluate the vessel extraction. The quantitative analysis method was validated using images of a three‐dimensional (3D) printed vessel phantom, scanned by a clinical CT scanner and a micro‐CT scanner (to obtain a gold standard). To confirm the association between imaging biomarkers and pulmonary function, 77 scleroderma patients were investigated with the proposed method.ResultsIn the independent evaluation with the public data set, our vessel segmentation method obtained an area under the receiver operating characteristic (ROC) curve of 0.976. The median radius difference between clinical and micro‐CT scans of a 3D printed vessel phantom was 0.062 ± 0.020 mm, with interquartile range of 0.199 ± 0.050 mm. In the studied patient group, a significant correlation between diffusion capacity for carbon monoxide and the biomarkers, α (R = −0.27, P = 0.018) and β (R = 0.321, P = 0.004), was obtained.ConclusionIn conclusion, the proposed method was validated independently using a public data set resulting in an area under the ROC curve of 0.976 and using a 3D printed vessel phantom data set, showing a vessel sizing error of 0.062 mm (0.16 in‐plane pixel units). The correlation between imaging biomarkers and diffusion capacity in a clinical data set confirmed an association between lung structure and function. This quantification of pulmonary vascular morphology may be helpful in understanding the pathophysiology of pulmonary vascular diseases.

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Linking Convolutional Neural Networks with Graph Convolutional Networks: Application in Pulmonary Artery-Vein Separation

Zhai

Staring

Zhou

et al. 2019

Lecture Notes in Computer Science

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Automated integer programming based separation of arteries and veins from thoracic CT images

Cited by 34 publications

References 39 publications

Deep-learning strategy for pulmonary artery-vein classification of non-contrast CT images

Deep-learning strategy for pulmonary artery-vein classification of non-contrast CT images

Automatic quantitative analysis of pulmonary vascular morphology in CT images

Linking Convolutional Neural Networks with Graph Convolutional Networks: Application in Pulmonary Artery-Vein Separation

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