Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture

Magnetic resonance cine imaging is the accepted standard for cardiac functional assessment. Left ventricular (LV) segmentation plays a key role in volumetric functional quantification of the heart. Conventional manual analysis is time-consuming and observer-dependent. Automated segmentation approaches are needed to improve the clinical workflow of cardiac functional quantification. Recently, deep-learning networks have shown promise for efficient LV segmentation.

The routinely used V-Net is a convolutional network that segments images by passing features from encoder to decoder. In this study, this method was advanced as DenseV-Net by replacing the convolutional block with a densely connected algorithm and dense calculations to alleviate the vanishing-gradient problem, prevent exploding gradients, and to strengthen feature propagation. Thirty patients were scanned with a 3 Tesla MR imager. ECG-free, free-breathing, real-time cines were acquired with a balanced steady-state free precession technique. Linear regression and the dice similarity coefficient (DSC) were performed to evaluate LV segmentation performance of the classic neural networks FCN, UNet, V-Net, and the proposed DenseV-net methods, using manual analysis as the reference. Slice-based LV function was compared among the four methods.

Thirty slices from eleven patients were randomly selected (each slice contained 73 images), and the LVs were segmented using manual analysis, UNet, FCN, V-Net, and the proposed DenseV-Net methods. A strong correlation of the left ventricular areas was observed between the proposed DenseV-Net network and manual segmentation (R = 0.92), with a mean DSC of 0.90 ± 0.12. A weaker correlation was found between the routine V-Net, UNet, FCN, and manual segmentation methods (R = 0.77, 0.74, 0.76, respectively) with a lower mean DSC (0.85 ± 0.13, 0.84 ± 0.16, 0.79 ± 0.17, respectively). Additionally, the proposed DenseV-Net method was strongly correlated with the manual analysis in slice-based LV function quantification compared with the state-of-art neural network methods V-Net, UNet, and FCN.

The proposed DenseV-Net method outperforms the classic convolutional networks V-Net, UNet, and FCN in automated LV segmentation, providing a novel way for efficient heart functional quantification and the diagnosis of cardiac diseases using cine MRI.