Automatic detection of anatomical landmarks of the aorta in CTA images

Tahoces, Pablo G.; Santana-Cedrés, Daniel; Álvarez, Luis; Alemán‐Flores, Miguel; Trujillo, Agustín; Cuenca, Carmelo; Carreira, J. M.

doi:10.1007/s11517-019-02110-x

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…Until 2016, Tahoces et al and two enhanced versions were developed. This network employs convolution with voids rather than traditional convolutional computing, employs various scales to improve spatial resolution of segmentation findings [ 24 ], and uses conditional random fields as postprocessing to refine the results [ 25 ]. Advances in modern medical imaging technology provide higher resolution medical image data for the diagnostic process while analyzing raw data is inefficient.…”

Section: Current Status Of Researchmentioning

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: Current Status Of Researchmentioning

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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“…Chen et al (Chen et al, 2018) present a semi-automatic method for artery tracing, labelling and feature extraction for the cerebral arteries, validated through comparing the bifurcation placement by the algorithm against a human observer, lacking full automation of the artery tracing and labelling processes. An automatic method for labelling of the main aortic branches and landmark detection is described in (Tahoces et al, 2020), missing automatic segmentation.…”

Section: Recent Workmentioning

confidence: 99%

A Fully Automatic Method for Vascular Tortuosity Feature Extraction in the Supra-Aortic Region: Unraveling Possibilities in Stroke Treatment Planning

et al. 2022

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“…In [2], the aorta lumen contour is estimated using an ellipse motion tracking. In [12], authors use the aorta centerline and the aorta segmentation as reference structures to detect anatomical landmarks of the aorta in CTA images.…”

Section: Related Workmentioning

confidence: 99%