基于改进U-Net网络的甲状腺结节超声图像分割方法

针对甲状腺结节尺寸多变、超声图像中甲状腺结节边缘模糊导致难以分割的问题，该文提出一种基于改进U-net网络的甲状腺结节超声图像分割方法。该方法首先将图片经过有残差结构和多尺度卷积结构的编码器路径进行降尺度特征提取；然后，利用带有注意力模块的跳跃长连接部分对特征张量进行边缘轮廓保持操作；最后，使用带有残差结构和多尺度卷积结构的解码器路径得到分割结果。实验结果表明，该文所提方法的平均分割Dice值达到0.7822，较传统U-Net方法具有更优的分割性能。

Ultrasound Image Segmentation Method of Thyroid Nodules Based on the Improved U-Net Network

An ultrasound image segmentation method of thyroid nodules based on the improved u-net network is proposed in this paper, in order to solve the problem of changeable size of thyroid nodules and difficulty in segmentation due to edge blur of thyroid nodules in the ultrasound image. Firstly, the image is downscaled to extract the features through an encoder path with a residual structure and a multi-scale convolution structure. Secondly, the long skip connection with an attention module is used to maintain the edge contour of characteristic tensor. Finally, the segmentation result is obtained by a decoder path with a residual structure and a multi-scale convolution structure. The experimental results show that with the method proposed in this paper, the average segmentation Dice value reaches 0.7822. It indicates that this method has better segmentation performance than the traditional U-Net method.