论文标题
使用bpmsegnet中的臂丛超声图像中的多个实例分割
Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet
论文作者
论文摘要
神经的识别很困难，因为神经的结构在超声图像中挑战和检测。然而，超声图像中的神经识别是改善区域麻醉性能的关键步骤。在本文中，提出了一个称为臂神经多构成分割网络（BPMSEGNET）的网络，以在超声图像中识别不同的组织（神经，动脉，静脉，肌肉）。 bpmsegnet有三个新型模块。第一个是空间局部对比功能，该功能在不同尺度上计算对比功能。第二个是自我发项门，它通过其重要性来重新获得特征地图中的频道。第三个是在特征金字塔网络中加入与转置卷积的添加。通过在我们构建的超声臂丛数据集（UBPD）上进行实验来评估所提出的BPMSEGNET。定量实验结果表明，所提出的网络可以从超声图像中分割多个组织，并具有良好的性能。
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.
