Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography
| Autor: | Liya Gu, Qiusheng Shen, Yanzhe Wang, Xiaogang Ren, Xuefeng Ji, Wenjuan Cai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Medicine (General)
Article Subject Computed Tomography Angiography Biomedical Engineering Health Informatics Coronary Angiography computer.software_genre R5-920 Sørensen–Dice coefficient Approximation error Voxel Medical technology Humans Segmentation Sensitivity (control systems) R855-855.5 Mathematics business.industry Pattern recognition Coronary Vessels Test set Line (geometry) Surgery Artificial intelligence business Tomography Spiral Computed Distance transform computer Algorithms Research Article Biotechnology |
| Zdroj: | Journal of Healthcare Engineering, Vol 2021 (2021) Journal of Healthcare Engineering |
| ISSN: | 2040-2309 2040-2295 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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