KEYWORDS: Image segmentation, Aorta, Magnetic resonance imaging, 3D modeling, Hemodynamics, Electroluminescence, Blood circulation, Deep learning, Education and training, Data modeling
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.
Bicuspid aortic valve (BAV) is a hereditary disorder that develops in the fetus at the early stages of pregnancy. Though the patient may have BAV defect at the time of birth, it may not be diagnosed until the patient becomes often symptomatic in adulthood. BAV patients are at a higher risk of aneurysm growth with a high mortality rate. Hence, measurements acquired from automated aortic segmentation would aid in faster analysis of hemodynamic parameters for better risk-stratify in BAV patients. In this work, we propose a fully automated segmentation tool using a deep learning technique for fast and accurate aortic segmentation. The 3D aorta volume was segmented based on the proposed model (U-Net++) and compared with two-dimensional (2D) deep convolutional neural network (DCNN) models (U-Net and Attention U-Net). Performance metrics such as Dice similarity coefficient (DSC), Hausdorff distance (HD), and absolute volume difference (AVD) were used for model evaluation. The proposed model reported the highest DSC of 0.88±0.02 on the dataset comprising of 114 subjects (n=91 BAV and n=23 healthy cases). The HD shows a difference in mean of 3.8mm between the manual and the predicted results. Though a limited dataset was deployed in this work, the model reports a high DSC based on 3D phase contrast (PC) magnetic resonance angiogram (MRA) (PCMRA) images obtained at a clinical setting. This fully automated approach minimizes the burdensome data analysis, data annotation cost and would aid for early diagnosis and to start individualized treatment to enhance the patient outcome.
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