基于可变形卷积和语义嵌入式注意力机制的眼球超声图像分割方法

眼球区域分割是医学超声图像处理和分析的关键步骤,由于临床设备采集的眼球超声图像具有噪声干扰、区域模糊、边缘灰度相似等缺点,从而导致现有的方法不能准确地分割出眼球区域,因此本文基于可变形卷积提出了一种语义嵌入的注意力机制的分割方法.首先使用可变形卷积替代传统的卷积,提高本文网络对眼球区域的表征能力;其次构建语义嵌入的注意...

Eyeball Ultrasound Image Segmentation Based on Deformable Convolution and Semantic Embedded Attention Mechanism

The segmentation of eyeball areas is a key step in medical ultrasound image processing and analysis. Since the eyeball ultrasound images collected by clinical equipment have disadvantages including noise interference, blurred areas, and similar edge gray levels, the existing methods cannot accurately segment eyeball areas. Therefore, this study proposes a semantic embedded attention mechanism for eyeball segmentation based on deformable convolutions. Firstly, deformable convolutions, instead of traditional convolutions, are used to improve the representational ability of the network in eyeball areas. Secondly, a semantic embedded attention mechanism is constructed to fuse semantic information among different layers, enhance the salient features in the target area, and reduce the wrong segmentation of the background area, thereby improving the segmentation accuracy of the network. Finally, in order to check the segmentation performance, the proposed model in this study is compared with three existing deep learning segmentation models, and it obtains the highest accuracy on the segmentation data set of ultrasound eyeball images, fully verifying that this model has better segmentation ability and robustness.