Tian Huijuan, Zhang Lei, Fu Xuetong, Zhang Hongyang, Wang Yuanquan, Zhou Shoujun, Wei Jin
School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin, 300401, China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Med Biol Eng Comput. 2024 Dec 14. doi: 10.1007/s11517-024-03255-0.
Echocardiography is a primary tool for cardiac diagnosis. Accurate delineation of the left ventricle is a prerequisite for echocardiography-based clinical decision-making. In this work, we propose an echocardiographic left ventricular segmentation method based on the diffusion probability model, which is named EchoSegDiff. The EchoSegDiff takes an encoder-decoder structure in the reverse diffusion process. A diffusion encoder residual block (DEResblock) based on the atrous pyramid squeeze attention (APSA) block is coined as the main module of the encoder, so that the EchoSegDiff can catch multiscale features effectively. A novel feature fusion module (FFM) is further proposed, which can adaptively fuse the features from encoder and decoder to reduce semantic gap between encoder and decoder. The proposed EchoSegDiff is validated on two publicly available echocardiography datasets. In terms of left ventricular segmentation performance, it outperforms other state-of-the-art networks. The segmentation accuracy on the two datasets reached 93.69% and 89.95%, respectively. This demonstrates the excellent potential of EchoSegDiff in the task of left ventricular segmentation in echocardiography.
超声心动图是心脏诊断的主要工具。准确描绘左心室是基于超声心动图的临床决策的前提条件。在这项工作中，我们提出了一种基于扩散概率模型的超声心动图左心室分割方法，名为EchoSegDiff。EchoSegDiff在反向扩散过程中采用编码器-解码器结构。基于空洞金字塔挤压注意力（APSA）块的扩散编码器残差块（DEResblock）被设计为编码器的主要模块，从而使EchoSegDiff能够有效地捕捉多尺度特征。进一步提出了一种新颖的特征融合模块（FFM），它可以自适应地融合来自编码器和解码器的特征，以减少编码器和解码器之间的语义差距。所提出的EchoSegDiff在两个公开可用的超声心动图数据集上得到了验证。在左心室分割性能方面，它优于其他现有的先进网络。在这两个数据集上的分割准确率分别达到了93.69%和89.95%。这证明了EchoSegDiff在超声心动图左心室分割任务中的优异潜力。
