Main Coronary Vessel Segmentation Using Deep Learning in Smart Medical

The automatic segmentation of main vessels on X-ray angiography (XRA) images is of great importance in the smart coronary artery disease diagnosis system. However, existing methods have been developed to this task, but these methods have difficulty in recognizing the coronary artery structure in XRA images. Main vessel segmentation is still a challenging task due to the diversity and small-size region of the vessel in the XRA images. In this study, we propose a robust method for main vessel segmentation by using deep learning architectures with fully convolutional networks. Four deep learning models based on the UNet architecture are evaluated on a clinical dataset, which consists of 3200 X-ray angiography images collected from 1118 patients. Using the precision (Pre), recall (Re), and F1 score (F1) as evaluation metrics, the average Pre, Re, and F1 for main vessel segmentation in the entire experimental dataset is 0.901, 0.898, and 0.900, respectively. 89.8% of the images exhibited a high F1 score >0.8. For the main vessel segmentation in XRA images, our deep learning methods demonstrated that vessels could be segmented in real time with a more optimized implementation, to further facilitate the online diagnosis in smart medical

The Impact of the Geometric Characteristics on the Hemodynamics in the Stenotic Coronary Artery.

The alterations of the hemodynamics in the coronary arteries, which result from patient-specific geometric significances are complex. The effect of the stenosis on the blood flow alteration had been wildly reported, but the combinational contribution from geometric factors required a comprehensive investigation to provide patient-specific information for diagnosis and assisting in the decision on the further treatment strategies. In the present study, we investigated the correlation between hemodynamic parameters and individual geometric factors in the patient-specific coronary arteries. Computational fluid dynamic simulations were performed on 22 patient-specific 3-dimensional coronary artery models that were reconstructed based on computed tomography angiography images. Our results showed that the increasing severity of the stenosis is associated with the increased maximum wall shear stress at the stenosis region (r = 0.752, P < 0.001). In contrast, the length of the recirculation zone has a moderate association with the curvature of the lesion segment (r = 0.505, P = 0.019) and the length of the lesions (r = 0.527, P = 0.064). Moreover, bifurcation in the coronary arteries is significantly correlated with the occurrence of recirculation, whereas the severity of distal stenosis demonstrated an effect on the alteration of the flow in the upstream bifurcation. These findings could serve as an indication for treatment planning and assist in prognosis evaluation

Hydrogen Sulfide-Preconditioning of Human Endothelial Progenitor Cells Transplantation Improves Re-Endothelialization in Nude Mice with Carotid Artery Injury

Background/Aims: The aim of present study was to test the hypothesis that preconditioning with sodium hydrosulfide (NaHS) could enhance the capacity of migration, adhesion and proliferation of endothelial progenitor cells (EPCs) in vitro, and also could improve the efficacy of EPCs transplantation for re-endothelialization in nude mice with carotid artery injury. The paper further addressed the underlying mechanisms. Methods: EPCs were isolated from peripheral blood mononuclear cells of healthy male volunteers and the markers of EPCs were analyzed by flow cytometry. Thereafter, different concentrations of NaHS (25, 50, 100, 200 and 500 uM) were used for preconditioning EPCs. In vitro and in vivo migration, adhesion and proliferation as well as nitric oxide (NO) production of EPCs were evaluated. Carotid artery injury model was produced in nude mice and thereafter, NaHS-preconditioned EPCs were transplanted in order to evaluate their capacity of re-endothelialization. Results: Cellular immuno-staining showed that isolated cells expressed the key markers of EPCs. In vitro, EPCs proliferation rates and NO production were gradually increased in a NaHS-concentration dependent manner, while these benefits were blocked at a concentration of 500 uM NaHS. Similarly, the migration and adhesion rates of EPCs were also increased the most prominently at a concentration of 200 µM NaHS. In vivo, compared to the control group, treatment with NaHS-preconditioned EPCs significantly enhanced the capacity of re-endothelialization of EPCs. Fluorescent microscope revealed that there were more EPCs homing to the injury vessels in the NaHS-preconditioned EPCs group than the non-preconditioned group. With the administration of AMPK or eNOS inhibitors respectively, the above benefits of NaHS-preconditioning were abrogated. Conclusion: These results suggested that NaHS-preconditioning enhanced the biological function and re-endothelialization of EPCs through the AMPK/eNOS signaling pathway

The effect of stenosis severity on the recirculation zone.

<p>Streamline of the flow distribution are illustrated (unit: m/s). The length the recirculation increase with the severity of the stenosis. Severity of the stenosis in A, B, C and D is 28.2%, 57%, 76.6% and 86.3% with the corresponding length of the recirculation zone is 0 mm, 5.02 mm, 6.48 mm to 16.77 mm, respectively. Recirculation zone is not only seen at the downstream of the stenosis, but also at the upstream bifurcation (labeled with red cross-star in C).</p

The streamlines of the flow distributions color-coded with velocity magnitude.

<p>Increasing of the resistance due to the distal stenosis leads to the flow reversal at the upstream bifurcation that blood flow is redistributed to the side branch as showed in A (unit: m/s). Examples of the disappearing of the recirculation zone at the downstream of the stenosis as showed in B (unit: m/s).</p

Relationship among stenosis severity, tortuosity, curvature, angle, lesion length with wall shear stress in the reconstructed patient-specific coronary arteries.

<p>A: effect of stenosis severity (percent diameter stenosis) on maximum wall shear stress; B: effect of tortuosity on maximum wall shear stress; C: effect of curvature on maximum wall shear stress; D: effect of lesion length on maximum wall shear stress; E: effect of angle of the lesion segment on maximum wall shear stress. Third-order nonlinear cur fit with 95% confident interval is shown in A with R² equals 0.521.</p

The streamline of the flow distribution color-coded with velocity magnitude.

<p>The pulsatile effect of the blood flow on the distribution of the recirculation. A, B, C and D illustrated 4 time points during one cardiac cycle. A: The recirculation disappeared at the low flow rate; B, C and D: The area of the recirculation zone varied along with the flow rate while the lengths of the recirculation zone were relatively consistence (5mm).</p

The distribution of the flow and the corresponding WSS distribution in the stenotic coronary artery.

<p>A: The streamline of the flow distribution color-coded with velocity magnitude showed that blood flow is separated at the downstream of the stenosis, leading to the occurrence of the recirculation zone (red arrow) (unit: m/s). Severe stenosis (>80%) resulted in the increasing of the resistance of the vascular bed, leading to the flow reversal at the upstream bifurcation (yellow cross-star). B: The vessel wall shear stress distribution showed that the maximum wall shear stress is found at the distal stenosis (red cross-star), disturbance of the flow is found in downstream, leading to the altered distribution of the wall shear stress (red frame) (unit: Pa). C and D: The streamline of the flow distribution color-coded with velocity magnitude in the cross-section area showed a more significant secondary flow pattern at the upstream (as in C labeled with yellow cross-star corresponding to the location showed in A) compare to that at the downstream of distal stenosis (as in D labeled with red arrow corresponding to the location showed in A) (unit: m/s).</p

The FFRCT distribution in the coronary arterial geometries with stenosis.

<p>The FFRCT at the stenosis decreased as the severity of the stenosis increased. Degree of the stenosis in A, B and C was 57%, 76,6% and 86.4%, the corresponding FFRCT value was 0.687, 0.603 and 0.57, respectively.</p

Validation of the calculation is provided by comparing calculated FFRCT to the measurement FFR.

<p>A: Agreement between the FFRCTA and FFR is evaluated by Bland-Altman agreement test, the mean±SD bias of is 0.00269 ± 0.01899. B: Agreement between FFRCT and FFR is also evaluated by linear regression that R² = 0.974 with 95% confident interval.</p