scholarly journals A Semi-Automatic Segmentation Method for the Structural Analysis of Carotid Atherosclerotic Plaques by Computed Tomography Angiography

2014 ◽  
Vol 21 (9) ◽  
pp. 930-940 ◽  
Author(s):  
Florentino Luciano Caetano dos Santos ◽  
Atte Joutsen ◽  
Mitsugu Terada ◽  
Juha Salenius ◽  
Hannu Eskola
Keyword(s):  
Computed Tomography ◽  
Structural Analysis ◽  
Computed Tomography Angiography ◽  
Automatic Segmentation ◽  
Atherosclerotic Plaques ◽  
Segmentation Method

scholarly journals 3D reconstruction of coronary arteries and atherosclerotic plaques based on computed tomography angiography images

2018 ◽  
Vol 40 ◽  
pp. 286-294 ◽  
Author(s):  
Vassiliki I. Kigka ◽  
George Rigas ◽  
Antonis Sakellarios ◽  
Panagiotis Siogkas ◽  
Ioannis O. Andrikos ◽  
...  
Keyword(s):  
Computed Tomography ◽  
3D Reconstruction ◽  
Coronary Arteries ◽  
Computed Tomography Angiography ◽  
Atherosclerotic Plaques

scholarly journals Consistency in Geometry Among Coronary Atherosclerotic Plaques Extracted From Computed Tomography Angiography

2021 ◽  
Vol 12 ◽  
Author(s):  
Haipeng Liu ◽  
Aleksandra Wingert ◽  
Xinhong Wang ◽  
Jucheng Zhang ◽  
Jianzhong Sun ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Power Function ◽  
Computed Tomography Angiography ◽  
Geometric Parameters ◽  
Atherosclerotic Plaques ◽  
Geometric Properties ◽  
Artery Disease ◽  
The Relationship

Background: The three-dimensional (3D) geometry of coronary atherosclerotic plaques is associated with plaque growth and the occurrence of coronary artery disease. However, there is a lack of studies on the 3D geometric properties of coronary plaques. We aim to investigate if coronary plaques of different sizes are consistent in geometric properties.Methods: Nineteen cases with symptomatic stenosis caused by atherosclerotic plaques in the left coronary artery were included. Based on attenuation values on computed tomography angiography images, coronary atherosclerotic plaques and calcifications were identified, 3D reconstructed, and manually revised. Multidimensional geometric parameters were measured on the 3D models of plaques and calcifications. Linear and non-linear (i.e., power function) fittings were used to investigate the relationship between multidimensional geometric parameters (length, surface area, volume, etc.). Pearson correlation coefficient (r), R-squared, and p-values were used to evaluate the significance of the relationship. The analysis was performed based on cases and plaques, respectively. Significant linear relationship was defined as R-squared > 0.25 and p < 0.05.Results: In total, 49 atherosclerotic plaques and 56 calcifications were extracted. In the case-based analysis, significant linear relationships were found between number of plaques and number of calcifications (r = 0.650, p = 0.003) as well as total volume of plaques (r = 0.538, p = 0.018), between number of calcifications and total volume of plaques (r = 0.703, p = 0.001) as well as total volume of calcification (r = 0.646, p = 0.003), and between the total volumes of plaques and calcifications (r = 0.872, p < 0.001). In plaque-based analysis, the power function showed higher R-squared values than the linear function in fitting the relationships of multidimensional geometric parameters. Two presumptions of plaque geometry in different growth stages were proposed with simplified geometric models developed. In the proposed models, the exponents in the power functions of geometric parameters were in accordance with the fitted values.Conclusion: In patients with coronary artery disease, coronary plaques and calcifications are positively related in number and volume. Different coronary plaques are consistent in the relationship between geometry parameters in different dimensions.

scholarly journals 3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks

2020 ◽  
Vol 11 (5) ◽  
pp. 576-586
Author(s):  
Alice Fantazzini ◽  
Mario Esposito ◽  
Alice Finotello ◽  
Ferdinando Auricchio ◽  
Bianca Pane ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Computed Tomography Angiography ◽  
Automatic Segmentation ◽  
Spatial Coherence ◽  
Ground Truth ◽  
Iliac Arteries ◽  
Single View ◽  
View Integration ◽  
Contrast Enhanced ◽  
Enhanced Computed Tomography

Abstract Purpose The quantitative analysis of contrast-enhanced Computed Tomography Angiography (CTA) is essential to assess aortic anatomy, identify pathologies, and perform preoperative planning in vascular surgery. To overcome the limitations given by manual and semi-automatic segmentation tools, we apply a deep learning-based pipeline to automatically segment the CTA scans of the aortic lumen, from the ascending aorta to the iliac arteries, accounting for 3D spatial coherence. Methods A first convolutional neural network (CNN) is used to coarsely segment and locate the aorta in the whole sub-sampled CTA volume, then three single-view CNNs are used to effectively segment the aortic lumen from axial, sagittal, and coronal planes under higher resolution. Finally, the predictions of the three orthogonal networks are integrated to obtain a segmentation with spatial coherence. Results The coarse segmentation performed to identify the aortic lumen achieved a Dice coefficient (DSC) of 0.92 ± 0.01. Single-view axial, sagittal, and coronal CNNs provided a DSC of 0.92 ± 0.02, 0.92 ± 0.04, and 0.91 ± 0.02, respectively. Multi-view integration provided a DSC of 0.93 ± 0.02 and an average surface distance of 0.80 ± 0.26 mm on a test set of 10 CTA scans. The generation of the ground truth dataset took about 150 h and the overall training process took 18 h. In prediction phase, the adopted pipeline takes around 25 ± 1 s to get the final segmentation. Conclusion The achieved results show that the proposed pipeline can effectively localize and segment the aortic lumen in subjects with aneurysm.

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