Song Haibei, Huang Zonghai, Feng Li, Zhong Yanmei, Wen Chuanbiao, Guo Jinhong
School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Digit Health. 2022 Nov 3;8:20552076221136362. doi: 10.1177/20552076221136362. eCollection 2022 Jan-Dec.
Due to the complexity of face images, tongue segmentation is susceptible to interference from uneven tongue texture, lips and face, resulting in traditional methods failing to segment the tongue accurately. To address this problem, RAFF-Net, an automatic tongue region segmentation network based on residual attention network and multiscale feature fusion, was proposed. It aims to improve tongue segmentation accuracy and achieve end-to-end automated segmentation.
Based on the UNet backbone network, different numbers of ResBlocks combined with the Squeeze-and-Excitation (SE) block was used as an encoder to extract image layered features. The decoder structure of UNet was simplified and the number of parameters of the network model was reduced. Meanwhile, the multiscale feature fusion module was designed to optimize the network parameters by combining a custom loss function instead of the common cross-entropy loss function to further improve the detection accuracy.
The RAFF-Net network structure achieved Mean Intersection over Union (MIoU) and F1-score of 97.85% and 97.73%, respectively, which improved 0.56% and 0.46%, respectively, compared with the original UNet; ablation experiments demonstrated that the improved algorithm could contribute to the enhancement of tongue segmentation effect.
This study combined the residual attention network with multiscale feature fusion to effectively improve the segmentation accuracy of the tongue region, and optimized the input and output of the UNet network using different numbers of ResBlocks, SE block, multiscale feature fusion and weighted loss function, increased the stability of the network and improved the overall effect of the network.
由于面部图像的复杂性，舌部分割容易受到不均匀舌纹理、嘴唇和面部的干扰，导致传统方法无法准确分割舌头。为了解决这个问题，提出了基于残差注意力网络和多尺度特征融合的自动舌区分割网络RAFF-Net。其目的是提高舌部分割精度并实现端到端自动分割。
基于UNet骨干网络，使用不同数量的与挤压激励（SE）块相结合的残差块作为编码器来提取图像分层特征。简化了UNet的解码器结构，减少了网络模型的参数数量。同时，设计了多尺度特征融合模块，通过结合自定义损失函数而非常见的交叉熵损失函数来优化网络参数，以进一步提高检测精度。
RAFF-Net网络结构的平均交并比（MIoU）和F1分数分别达到97.85%和97.73%，与原始UNet相比分别提高了0.56%和0.46%；消融实验表明改进算法有助于增强舌部分割效果。
本研究将残差注意力网络与多尺度特征融合相结合，有效提高了舌区的分割精度，并使用不同数量的残差块、SE块、多尺度特征融合和加权损失函数对UNet网络的输入和输出进行优化，增加了网络的稳定性，提高了网络的整体效果。
