Yao Yuan, Zhang Zhenguang, Peng Bo, Tang Jin
General Practice Medical Center, West China Hospital, Sichuan University, Chengdu 610044, China.
School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.
Bioengineering (Basel). 2023 Jun 26;10(7):768. doi: 10.3390/bioengineering10070768.
Using ultrasound imaging to diagnose liver steatosis is of great significance for preventing diseases such as cirrhosis and liver cancer. Accurate diagnosis under conditions of low quality, noise and poor resolutions is still a challenging task. Physiological studies have shown that the visual cortex of the biological visual system has selective attention neural mechanisms and feedback regulation of high features to low features. When processing visual information, these cortical regions selectively focus on more sensitive information and ignore unimportant details, which can effectively extract important features from visual information. Inspired by this, we propose a new diagnostic network for hepatic steatosis. In order to simulate the selection mechanism and feedback regulation of the visual cortex in the ventral pathway, it consists of a receptive field feature extraction module, parallel attention module and feedback connection. The receptive field feature extraction module corresponds to the inhibition of the non-classical receptive field of V1 neurons on the classical receptive field. It processes the input image to suppress the unimportant background texture. Two types of attention are adopted in the parallel attention module to process the same visual information and extract different important features for fusion, which improves the overall performance of the model. In addition, we construct a new dataset of fatty liver ultrasound images and validate the proposed model on this dataset. The experimental results show that the network has good performance in terms of sensitivity, specificity and accuracy for the diagnosis of fatty liver disease.
利用超声成像诊断肝脂肪变性对于预防肝硬化和肝癌等疾病具有重要意义。在低质量、有噪声和分辨率差的条件下进行准确诊断仍然是一项具有挑战性的任务。生理学研究表明，生物视觉系统的视觉皮层具有选择性注意神经机制以及从高特征到低特征的反馈调节。在处理视觉信息时，这些皮层区域选择性地聚焦于更敏感的信息，而忽略不重要的细节，这可以有效地从视觉信息中提取重要特征。受此启发，我们提出了一种新的肝脂肪变性诊断网络。为了模拟腹侧通路中视觉皮层的选择机制和反馈调节，它由一个感受野特征提取模块、并行注意力模块和反馈连接组成。感受野特征提取模块对应于V1神经元的非经典感受野对经典感受野的抑制作用。它对输入图像进行处理以抑制不重要的背景纹理。并行注意力模块采用两种类型的注意力来处理相同的视觉信息并提取不同的重要特征进行融合，从而提高了模型的整体性能。此外，我们构建了一个新的脂肪肝超声图像数据集，并在该数据集上对所提出的模型进行了验证。实验结果表明，该网络在诊断脂肪肝疾病的敏感性、特异性和准确性方面具有良好的性能。
