Validation of right coronary artery lumen area from cardiac computed tomography against intravascular ultrasound

Cui, Hengfei; Xia, Yong; Zhang, Yanning; Liu, Zhong

doi:10.1007/s00138-018-0978-z

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…It is estimated that presently 290 million people are afflicted with heart-related diseases within our country, close to 21% of the total population. Among these patients, the vast majority suffer from hypertension, amounting to 270 million, and the population suffering from coronary heart disease amounts to 11 million [ 2 ]. Heart-related diseases account for 40% of the composition of the population's mortality factors, more than other diseases such as cancer.…”

Section: Introductionmentioning

confidence: 99%

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

Cai

Wang

et al. 2021

Journal of Healthcare Engineering

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This paper presents an in-depth study and analysis of the 3D arterial centerline in spiral CT coronary angiography, and constructs its detection and extraction technique. The first time, the distance transform is used to complete the boundary search of the original figure; the second time, the distance transform is used to calculate the value of the distance transform of all voxels, and according to the value of the distance transform, unnecessary voxels are deleted, to complete the initial contraction of the vascular region and reduce the computational consumption in the next process; then, the nonwitnessed voxels are used to construct the maximum inner joint sphere model and find the skeletal voxels that can reflect the shape of the original figure. Finally, the skeletal lines were optimized on these initially extracted skeletal voxels using a dichotomous-like principle to obtain the final coronary artery centerline. Through the evaluation of the experimental results, the algorithm can extract the coronary centerline more accurately. In this paper, the segmentation method is evaluated on the test set data by two kinds of indexes: one is the index of segmentation result evaluation, including dice coefficient, accuracy, specificity, and sensitivity; the other is the index of clinical diagnosis result evaluation, which is to refine the segmentation result for vessel diameter detection. The results obtained in this paper were compared with the physicians’ labeling results. In terms of network performance, the Dice coefficient obtained in this paper was 0.89, the accuracy was 98.36%, the sensitivity was 93.36%, and the specificity was 98.76%, which reflected certain advantages in comparison with the advanced methods proposed by previous authors. In terms of clinical evaluation indexes, by performing skeleton line extraction and diameter calculation on the results obtained by the segmentation method proposed in this paper, the absolute error obtained after comparing with the diameter of the labeled image was 0.382 and the relative error was 0.112, which indicates that the segmentation method in this paper can recover the vessel contour more accurately. Then, the results of coronary artery centerline extraction with and without fine branch elimination were evaluated, which proved that the coronary artery centerline has higher accuracy after fine branch elimination. The algorithm is also used to extract the centerline of the complete coronary artery tree, and the results prove that the algorithm has better results for the centerline extraction of the complete coronary vascular tree.

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Section: Introductionmentioning

confidence: 99%

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

Cai

Wang

et al. 2021

Journal of Healthcare Engineering

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“…performed for coronary IVUS images [2,3] graph search, active surfaces, and active contours, were applied to segment IVUS images based on local image properties or global gray level properties. 3D fast marching method [4,5], incorporating the texture gradient and the gray level gradient, was applied to segment the walls of the coronary artery with an interactive initialization on EEM borders. In recent years, deep learning has been widely applied in the medical imaging analysis and achieved remarkable results [6,7].…”

Section: Several Segmentation Techniques and Methods In Image Processmentioning

confidence: 99%

Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

Dong

Jiang

et al. 2020

Preprint

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Background: Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully-automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane, i.e. EEM cross section area (EEM-CSA). The database comprises of single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images.Results: The mean intersection of union (mIoU) of 0.941 and 0.750 for the lumen and EEM-CSA respectively were achieved, which exceeded the manual labeling accuracy of the clinician. Conclusion: The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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“…Traditional image processing methods, including graph search, active surfaces, and active contours, were applied to segment IVUS images based on local image properties or global gray-level properties [ 4 ]. 3D fast marching method [ 5 , 6 ], incorporating the texture gradient and the gray-level gradient, was applied to segment the walls of the coronary artery with an interactive initialization on EEM borders. In recent years, deep learning has been widely applied in the medical imaging analysis and achieved remarkable results [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of coronary lumen and external elastic membrane in intravascular ultrasound images using 8-layer U-Net

Liang

Jiang

Wei³

et al. 2021

BioMed Eng OnLine

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Background Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane (EEM), i.e., cross-sectional area (EEM-CSA). The database comprises single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images. Results The mean intersection of union (MIoU) of 0.937 and 0.804 for the lumen and EEM-CSA, respectively, were achieved, which exceeded the manual labeling accuracy of the clinician. Conclusion The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D-IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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Validation of right coronary artery lumen area from cardiac computed tomography against intravascular ultrasound

Cited by 11 publications

References 30 publications

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

Automatic segmentation of coronary lumen and external elastic membrane in intravascular ultrasound images using 8-layer U-Net

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