tongue crack segmentation is an essential component of computer-aided diagnosis applied in Traditional Chinese Medicine (TCM). However, existing methods are inadequate when dealing with the vague boundary of the foreg...
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ISBN:
(纸本)9781728162157
tongue crack segmentation is an essential component of computer-aided diagnosis applied in Traditional Chinese Medicine (TCM). However, existing methods are inadequate when dealing with the vague boundary of the foreground and the variation of tongue images. To this end, we propose a P-shaped neural network architecture based on the lightweight encoder-decoder structure: the encoder transforms pixel position information into channel information by aggregating adjacent pixel values;the decoder restores the image size and obtains the refined pixel-level extraction results by integrating the information of the corresponding layer in the encoder. To further improve the utilization of network parameters and the model's generalization ability, we design three novel sub-modules: (1) the phantom module utilizes cheap operations to generate feature maps, speeding up the calculation;(2) the dual-input module increases the original input information to enhance the model's foreground understanding;(3) the dual attention gate module strengthens the information fusion of high-level and low-level feature maps, retaining good boundary information while capturing detail information. Additionally, we propose a pre-training method based on cropped patch images, which makes the model sensitive to details of the foreground before formal training. We demonstrate the model's effectiveness on our constructed dataset, achieving 60.6% IoU accuracy, and the segmentation of a 513x513 image takes 390 ms on CPU. And our dataset is available at https://***/pengjianqiang/FDU-TC.
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