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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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