Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet
This work aims to improve the accuracy of nerve identification in ultrasound images for medical professionals performing regional anesthesia, which is an incremental improvement in a domain-specific application.
This paper addresses the challenge of identifying nerve structures in ultrasound images, which is crucial for improving regional anesthesia. The authors propose BPMSegNet, a network designed to segment multiple tissues (nerves, arteries, veins, muscles) in brachial plexus ultrasound images, demonstrating good performance on their custom dataset.
The identification of nerve is difficult as structures of nerves are challenging to image and to detect in ultrasound images. Nevertheless, the nerve identification in ultrasound images is a crucial step to improve performance of regional anesthesia. In this paper, a network called Brachial Plexus Multi-instance Segmentation Network (BPMSegNet) is proposed to identify different tissues (nerves, arteries, veins, muscles) in ultrasound images. The BPMSegNet has three novel modules. The first is the spatial local contrast feature, which computes contrast features at different scales. The second one is the self-attention gate, which reweighs the channels in feature maps by their importance. The third is the addition of a skip concatenation with transposed convolution within a feature pyramid network. The proposed BPMSegNet is evaluated by conducting experiments on our constructed Ultrasound Brachial Plexus Dataset (UBPD). Quantitative experimental results show the proposed network can segment multiple tissues from the ultrasound images with a good performance.