An adaptive attention U-network for recognizing ultrasound images
Shengyu Jin, Jintao Duan, Zhanheng Chen, Fangfang Chen, Wei Fang, Miao Zhou, Qinghua Wu, Liangqing Lin, Zui ZouObjective
The traditional method of intraspinal anesthesia relies on surface anatomical landmarks for positioning, which is associated with a low accuracy rate. In addition, the procedure remains challenging, and the identification of anatomical structures is complex. This study aimed to develop an adaptive attention U-network to enhance the segmentation performance of spinal structures under ultrasound images.
Methods
Ultrasound videos of the spines were collected from 80 pregnant women, yielding a total of 1000 annotated images that were used to establish a novel database, spine ultrasound image dataset. Adaptive attention U-network uses the multidepth convolution kernel and adaptive local channel attention modules to effectively extract multiscale features. Subsequently, the global attention gate module and multiscale adaptive dynamic modulation were introduced to capture critical features and enhance image super-resolution performance. Comprehensive experiments were conducted on the spine ultrasound image dataset and public breast ultrasound images dataset, in which adaptive attention U-network was juxtaposed with other current medical image segmentation models using metrics including dice similarity coefficient.
Results
On the spine ultrasound image dataset, adaptive attention U-network achieved a mean dice similarity coefficient of 0.905. In external validation using the breast ultrasound images dataset, the network's segmentation of benign tumor structures reached a dice similarity coefficient of 0.857, demonstrating superior generalization capabilities. Adaptive attention U-network demonstrated consistent segmentation stability across all tested structures.
Conclusions
The proposed adaptive attention U-network significantly enhances the segmentation accuracy for spinal anatomical structures in ultrasound images, demonstrating superior precision compared with existing methods.