Fan Xiaole, Feng Xiufang
College of Software, Taiyuan University of Technology, Taiyuan 030024, China.
Displays. 2023 Apr;77:102395. doi: 10.1016/j.displa.2023.102395. Epub 2023 Feb 14.
Segmenting regions of lung infection from computed tomography (CT) images shows excellent potential for rapid and accurate quantifying of Coronavirus disease 2019 (COVID-19) infection and determining disease development and treatment approaches. However, a number of challenges remain, including the complexity of imaging features and their variability with disease progression, as well as the high similarity to other lung diseases, which makes feature extraction difficult. To answer the above challenges, we propose a new sequence encoder and lightweight decoder network for medical image segmentation model (SELDNet). (i) Construct sequence encoders and lightweight decoders based on Transformer and deep separable convolution, respectively, to achieve different fine-grained feature extraction. (ii) Design a semantic association module based on cross-attention mechanism between encoder and decoder to enhance the fusion of different levels of semantics. The experimental results showed that the network can effectively achieve segmentation of COVID-19 infected regions. The dice of the segmentation result was 79.1%, the sensitivity was 76.3%, and the specificity was 96.7%. Compared with several state-of-the-art image segmentation models, our proposed SELDNet model achieves better results in the segmentation task of COVID-19 infected regions.
从计算机断层扫描（CT）图像中分割出肺部感染区域，在快速准确地量化2019冠状病毒病（COVID-19）感染以及确定疾病发展和治疗方法方面显示出巨大潜力。然而，仍存在一些挑战，包括成像特征的复杂性及其随疾病进展的变异性，以及与其他肺部疾病的高度相似性，这使得特征提取变得困难。为应对上述挑战，我们提出了一种用于医学图像分割模型的新型序列编码器和轻量级解码器网络（SELDNet）。（i）分别基于Transformer和深度可分离卷积构建序列编码器和轻量级解码器，以实现不同粒度的特征提取。（ii）设计一种基于编码器和解码器之间交叉注意力机制的语义关联模块，以增强不同层次语义的融合。实验结果表明，该网络能够有效地实现对COVID-19感染区域的分割。分割结果的Dice系数为79.1%，灵敏度为76.3%，特异性为96.7%。与几种先进的图像分割模型相比，我们提出的SELDNet模型在COVID-19感染区域的分割任务中取得了更好的结果。
