Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.
Biomed Phys Eng Express. 2020 Feb 18;6(2):025009. doi: 10.1088/2057-1976/ab7363.
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.
磁共振电影成像是心脏功能评估的公认标准。左心室（LV）分段在心脏容积功能定量中起着关键作用。传统的手动分析既耗时又依赖于观察者。需要自动化的分段方法来提高心脏功能定量的临床工作流程。最近，深度学习网络在高效 LV 分段方面显示出了前景。
常规使用的 V-Net 是一种通过从编码器向解码器传递特征来对图像进行分段的卷积网络。在这项研究中，通过用密集连接算法和密集计算代替卷积块，将该方法作为 DenseV-Net 进行了改进，以减轻消失梯度问题、防止梯度爆炸，并增强特征传播。用 3.0T 磁共振成像仪对 30 例患者进行扫描。使用平衡稳态自由进动技术采集无心电图、自由呼吸、实时电影。使用手动分析作为参考，对经典神经网络 FCN、UNet、V-Net 和所提出的 DenseV-net 方法进行 LV 分段性能的线性回归和骰子相似系数（DSC）评估。在四种方法之间比较了基于切片的 LV 功能。
从 11 例患者中随机选择 30 个切片（每个切片包含 73 张图像），使用手动分析、UNet、FCN、V-Net 和所提出的 DenseV-Net 方法对左心室进行分段。所提出的 DenseV-Net 网络与手动分割之间观察到左心室区域具有很强的相关性（R=0.92），平均 DSC 为 0.90±0.12。与常规 V-Net、UNet、FCN 和手动分割方法（R=0.77、0.74、0.76 分别）相比，相关性较弱，平均 DSC 较低（0.85±0.13、0.84±0.16、0.79±0.17 分别）。此外，与基于经典的卷积网络 V-Net、UNet 和 FCN 的方法相比，所提出的 DenseV-Net 方法在基于切片的 LV 功能定量方面与手动分析具有很强的相关性。
与经典的卷积网络 V-Net、UNet 和 FCN 相比，所提出的 DenseV-Net 方法在自动化 LV 分段方面表现出色，为使用电影 MRI 进行高效心脏功能定量和心脏疾病诊断提供了一种新方法。
