Li Changling, Song Xiangfen, Zhao Hang, Feng Li, Hu Tao, Zhang Yuchen, Jiang Jun, Wang Jianan, Xiang Jianping, Sun Yong
Department of Cardiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China.
ArteryFlow Technology Co., Ltd., Hangzhou, 310051, China.
Comput Methods Programs Biomed. 2021 Mar;200:105876. doi: 10.1016/j.cmpb.2020.105876. Epub 2020 Nov 26.
Accurate segmentation of left ventricle (LV) is a fundamental step in evaluation of cardiac function. Cardiac CT angiography (CCTA) has become an important clinical diagnostic method for cardio-vascular disease (CVD) due to its non-invasive, short exam time, and low cost. To obtain the segmentation of the LV in CCTA scans, we present a deep learning method based on an 8-layer residual U-Net with deep supervision.
Based on the original 4-layer U-Net, our method deepened the network to eight layers, which increased the fitting capacity of the network, thus greatly improved its LV recognition capability. Residual blocks were incorporated to optimize the network from the increased depth. Auxiliary paths as deep supervision were introduced to supervise the intermediate information to improve the segmentation quality. In this study, we collected CCTA scans of 100 patients. Eighty patients with 1600 discrete slices were used to train the LV segmentation and the remaining 20 patients with 400 discrete slices were used for testing our method. An interactive graph cut algorithm was utilized reliably to annotate the LV reference standard that was further confirmed by cardiologists. Online data augmentation was performed in the training process to improve the generalization and robustness of our method.
Compared with the segmentation results from the original U-Net and FC-DenseNet56 with Dice similarity coefficient (DSC) of 0.878±0.230 and 0.897±0.189, respectively, our method demonstrated higher segmentation accuracy and robustness for varying LV shape, size, and contrast, achieving DSC of 0.927±0.139. Without online data augmentation, our method resulted in inferior performance with DSC of 0.911±0.170. In addition, compared with the provided results from other existing studies in the LV segmentation of cardiac CT images, our method achieved a competitive performance for the LV segmentation.
The proposed 8-layer residual U-Net with deep supervision accurately and efficiently segments the LV in CCTA scans. This method has potential advantages to be a reliable segmentation method and useful for the evaluation of cardiac function in the future study.
准确分割左心室（LV）是评估心脏功能的基本步骤。心脏CT血管造影（CCTA）因其无创、检查时间短和成本低，已成为心血管疾病（CVD）重要的临床诊断方法。为了在CCTA扫描中获得左心室的分割结果，我们提出了一种基于具有深度监督的8层残差U-Net的深度学习方法。
基于原始的4层U-Net，我们的方法将网络加深到8层，这增加了网络的拟合能力，从而大大提高了其左心室识别能力。引入残差块以从增加的深度优化网络。引入辅助路径作为深度监督来监督中间信息以提高分割质量。在本研究中，我们收集了100例患者的CCTA扫描数据。80例患者的1600个离散切片用于训练左心室分割，其余20例患者的400个离散切片用于测试我们的方法。使用交互式图割算法可靠地标注左心室参考标准，并由心脏病专家进一步确认。在训练过程中进行在线数据增强，以提高我们方法的泛化能力和鲁棒性。
与原始U-Net和FC-DenseNet56的分割结果相比，其Dice相似系数（DSC）分别为0.878±0.230和0.897±0.189，我们的方法在不同左心室形状、大小和对比度下表现出更高的分割准确性和鲁棒性，DSC达到0.927±0.139。没有在线数据增强时，我们的方法性能较差，DSC为0.911±0.170。此外，与其他现有研究在心脏CT图像左心室分割中提供的结果相比，我们的方法在左心室分割方面具有竞争力。
所提出的具有深度监督的8层残差U-Net能够准确有效地分割CCTA扫描中的左心室。该方法具有成为可靠分割方法的潜在优势，并且在未来研究中对心脏功能评估有用。
