Song Along, Xu Lisheng, Wang Lu, Wang Bin, Yang Xiaofan, Xu Bu, Yang Benqiang, Greenwald Stephen E
IEEE J Biomed Health Inform. 2022 Aug;26(8):4044-4055. doi: 10.1109/JBHI.2022.3169425. Epub 2022 Aug 11.
Automatic coronary artery segmentation is of great value in diagnosing coronary disease. In this paper, we propose an automatic coronary artery segmentation method for coronary computerized tomography angiography (CCTA) images based on a deep convolutional neural network. The proposed method consists of three steps. First, to improve the efficiency and effectiveness of the segmentation, a 2D DenseNet classification network is utilized to screen out the non-coronary-artery slices. Second, we propose a coronary artery segmentation network based on the 3D-UNet, which is capable of extracting, fusing and rectifying features efficiently for accurate coronary artery segmentation. Specifically, in the encoding process of the 3D-UNet network, we adapt the dense block into the 3D-UNet so that it can extract rich and representative features for coronary artery segmentation; In the decoding process, 3D residual blocks with feature rectification capability are applied to improve the segmentation quality further. Third, we introduce a Gaussian weighting method to obtain the final segmentation results. This operation can highlight the more reliable segmentation results at the center of the 3D data blocks while weakening the less reliable segmentations at the block boundary when merging the segmentation results of spatially overlapping data blocks. Experiments demonstrate that our proposed method achieves a Dice Similarity Coefficient (DSC) value of 0.826 on a CCTA dataset constructed by us. The code of the proposed method is available at https://github.com/alongsong/3D_CAS.
自动冠状动脉分割在冠心病诊断中具有重要价值。在本文中，我们提出了一种基于深度卷积神经网络的用于冠状动脉计算机断层血管造影（CCTA）图像的自动冠状动脉分割方法。所提出的方法包括三个步骤。首先，为了提高分割的效率和有效性，利用二维密集连接网络（DenseNet）分类网络筛选出非冠状动脉切片。其次，我们提出了一种基于三维U型网络（3D-UNet）的冠状动脉分割网络，它能够有效地提取、融合和校正特征以实现准确的冠状动脉分割。具体而言，在3D-UNet网络的编码过程中，我们将密集块应用于3D-UNet，以便它能够为冠状动脉分割提取丰富且具有代表性的特征；在解码过程中，应用具有特征校正能力的三维残差块以进一步提高分割质量。第三，我们引入高斯加权方法来获得最终的分割结果。在合并空间重叠数据块的分割结果时，此操作可以突出三维数据块中心更可靠的分割结果，同时削弱块边界处不太可靠的分割结果。实验表明，我们提出的方法在我们构建的CCTA数据集上实现了0.826的骰子相似系数（DSC）值。所提出方法的代码可在https://github.com/alongsong/3D_CAS获取。
