TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segment...

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Vydáno v:Computational and mathematical methods in medicine Ročník 2020; číslo 2020; s. 1 - 13
Hlavní autoři: Li, Xiaoguang, Zhuo, Li, Zhang, Hui, Huang, Xiaodong, Zhang, Jing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
Témata:
Computational Biology
Databases, Factual
Deep Learning
Humans
Image Interpretation, Computer-Assisted - methods
Image Interpretation, Computer-Assisted - statistics & numerical data
Mathematical Concepts
Medicine, Chinese Traditional - methods
Medicine, Chinese Traditional - statistics & numerical data
Neural Networks, Computer
Tongue - diagnostic imaging
Tongue Diseases - diagnostic imaging
ISSN:1748-670X, 1748-6718, 1748-6718
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Shrnutí:Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels. Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body. The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively. The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+). There were also similar results on the HIT-tongue dataset. The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.
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Academic Editor: Seiya Imoto
ISSN:1748-670X
1748-6718
1748-6718
DOI:10.1155/2020/6029258