Ye Zi, Kumar Yogan Jaya, Song Fengyan, Li Guanxi, Zhang Suyu
School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325035, China.
Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka 76100, Malaysia.
Life (Basel). 2023 Apr 18;13(4):1040. doi: 10.3390/life13041040.
Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure's effectiveness.
左心室分割是评估心脏收缩和舒张功能的重要且必要的步骤，而超声心动图是一种不可或缺的诊断技术，可用于评估心脏功能。然而，在超声心动图图像上手动标记左心室区域既耗时又会导致观察者偏差。最近的研究表明，深度学习有能力自动进行分割过程。然而，不利的一面是，它在分割过程中仍然忽略了所有语义信息的贡献。本研究提出了一种基于BiSeNet的深度神经网络架构，名为Bi-DCNet。该模型包括一个空间路径和一个上下文路径，前者负责空间特征（低级）获取，后者负责上下文语义特征（高级）利用。此外，它通过整合扩张卷积进行特征提取，以获得更大的感受野来捕捉多尺度信息。使用EchoNet-Dynamic数据集对所提出的模型进行评估，这是在这个大型临床视频数据集上实现的第一个双边结构网络，用于完成左心室的分割。实验结果表明，我们的方法在DSC和IoU中分别获得了0.9228和0.8576，证明了该结构的有效性。
