Tonguenet: Accurate Localization and Segmentation for Tongue Images Using Deep Neural Networks
Tonguenet: Accurate Localization and Segmentation for Tongue Images Using Deep Neural Networks
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DOI：
10.1109/access.2019.2946681
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Li, Zuoyong
中科院分区：
文献类型：
--
作者：
Zhou, Changen;Fan, Haoyi;Li, Zuoyong
舌诊是中医监测人体健康状况的重要手段。作为实现自动舌诊断的关键步骤，对舌图像中舌体进行鲁棒、准确的分割和识别的主要挑战在于，不同患者因不同疾病而引起的舌外观（例如舌纹理和舌苔）存在较大差异。为了应对这些挑战，我们提出了一种新颖的用于舌头定位和分割的多任务学习的端到端模型，称为TongueNet，其中像素级先验信息用于深度卷积神经网络的监督训练。首先，我们引入了基于设计的上下文感知残差块的特征金字塔网络，用于提取多尺度舌头特征。然后，从提取的特征图中预先定位候选舌头的感兴趣区域（ROI）。最后，基于ROI的特征图对舌体进行更精细的定位和分割。对现实世界数据集的定量和定性比较表明，所提出的 TongueNet 在鲁棒性和准确性方面实现了舌体分割的最先进性能。
Tongue diagnosis is an important way of monitoring human health status in traditional Chinese medicine. As a key step of achieving automatic tongue diagnosis, the major challenges for robust and accurate segmentation and identification of tongue body in tongue images lay in the large variations of tongue appearance, e.g., tongue texture and tongue coating, caused by different diseases for different patients. To cope with these challenges, we propose a novel end-to-end model for multi-task learning of tongue localization and segmentation, named TongueNet, in which pixel-level prior information is utilized for supervised training of deep convolutional neural network. Firstly, we introduce a feature pyramid network based on the designed context-aware residual blocks for the extraction of multi-scale tongue features. Then, the region of interests (ROIs) of tongue candidates are located in advance from the extracted feature maps. Finally, finer localization and segmentation of tongue body are conducted based on the feature maps of ROIs. Quantitative and qualitative comparisons on real-world datasets show that the proposed TongueNet achieves state-of-the-art performance for the segmentation of tongue body in terms of both robustness and accuracy.