  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.
