Ultrasound Median Nerve Image Instance Segmentation via Nesting Attention and Boundary-guided Segmentation Mechanism

Most existing deep learning approaches, such as instance segmentation, are for natural images only. However, due to the unique characteristics of medical ultrasound images, they may not be suitable for ultrasound image diagnosis. In this study, we introduce Boundmask, an instance segmentation framework specially designed for medical ultrasound median nerve images. In Boundmark, firstly, we propose the nesting attention module (NAM), which combines spatial and channel attention to enhance the feature information so that we can still get rich feature information even with a simple backbone. Secondly, we design a boundary-guided segmentation mechanism (BGSM) that considers the object's unique traits and border information while segmenting. The experiments conducted using clinical data demonstrate that Boundmask has a high practical value. The results show that it achieves 54.2 AP on the ultrasound median nerve image dataset and outperforms most existing instance segmentation models.