Automated aortic segmentation using deep learning achieved a Dice score of 0.90 and calculated wall shear stress with a 6.62% mean difference compared to manual segmentation (P=0.94).
Does automated deep learning-based aortic segmentation accurately quantify hemodynamic parameters compared to manual segmentation in patients with bicuspid aortic valve undergoing 4D flow MRI?
A deep learning-based automated segmentation tool for 4D flow MRI accurately quantifies aortic hemodynamic parameters in patients with bicuspid aortic valve, performing comparably to manual expert segmentation.
Effect estimate: mean difference 6.62%
p-value: p=0.94
PurposeTo develop an automated method for aortic segmentation using deep learning techniques and further analyze the hemodynamic parameters in patients with bicuspid aortic valve (BAV). Since four-dimensional (4D) flow magnetic resonance imaging (MRI) imaging helps in analyzing and quantifying the blood flow changes that occur in aortic valve-related problems, such as BAV, 4D flow MRI images are considered.ApproachOur dataset consisted of 91 patients who had referral indications of BAV and 30 healthy volunteers who had no known cardiovascular disease. A U-Net++ with pretrained ResNet-34 encoders was trained for aortic segmentation using manual segmentation by an expert as the ground truth. In the first stage, the model was evaluated on 21 test cohorts using overlay and distance-based metrics, such as Dice score, Hausdorff distance, and absolute volume difference. In the second stage, the hemodynamic parameters, such as wall shear stress (WSS), viscous energy loss, and vorticity, were calculated to quantify the blood flow irregularities that occur in BAV patients. The segmentation and the flow parameters generated by the algorithm were compared with those generated using the manual segmentations. Paired t-test with alpha value of 0.05 was used for statistical significance testing.ResultsAs for overlap and distance-based metrics, the developed algorithm reported a Dice score coefficient of 0.90 ± 0.03, absolute volume difference of 1683 ± 1139 mm3, and Hausdorff distance of 3.2 ± 1.18 mm on test cohorts. The hemodynamic parameters calculated between automated and manual methods resulted in a mean difference of 6.62% for WSS with p-value of 0.94, 17.35% for mean viscous energy loss with p-value of 0.78, and 7.59% for vorticity with p-value of 0.97.ConclusionsA fast and accurate segmentation tool was developed for aortic segmentation using a dataset taken at clinical and blood flow parameters that were calculated based on the segmented aorta. These results will assist the clinicians to analyze the blood flow patterns and commence distinguished treatment in BAV patients.
Manokaran et al. (Tue,) conducted a other in Bicuspid aortic valve (n=121). Automated aortic segmentation using deep learning (U-Net++) vs. Manual segmentation was evaluated on Wall shear stress (WSS) difference between automated and manual methods (mean difference 6.62%, p=0.94). Automated aortic segmentation using deep learning achieved a Dice score of 0.90 and calculated wall shear stress with a 6.62% mean difference compared to manual segmentation (P=0.94).
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