BPSegSys: A Brachial Plexus Nerve Trunk Segmentation System Using Deep Learning

Objective Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthesia method that can be used to observe the target nerve and its surrounding structures, the puncture needle's advancement and local anesthetics spread in real time. The key in UGNB is nerve identification. With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently. Methods We established a public data set containing 320 ultrasound images of brachial plexus (BP). Three experienced doctors jointly produced the BP segmentation ground truth and labeled brachial plexus trunks. We designed a brachial plexus segmentation system (BPSegSys) based on deep learning. Results BPSegSys achieves experienced-doctor-level nerve identification performance in various experiments. We evaluated BPSegSys performance in terms of intersection-over-union (IoU). Considering three data set groups in our established public data set, the IoUs of BPSegSys were 0.5350, 0.4763 and 0.5043, respectively, which exceed the IoUs 0.5205, 0.4704 and 0.4979 of experienced doctors. In addition, we determined that BPSegSys can help doctors identify brachial plexus trunks more accurately, with IoU improvement up to 27%, which has significant clinical application value. Conclusion We establish a data set for brachial plexus trunk identification and designed a BPSegSys to identify the brachial plexus trunks. BPSegSys achieves the doctor-level identification of the brachial plexus trunks and improves the accuracy and efficiency of doctors' identification of the brachial plexus trunks. Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthesia method that can be used to observe the target nerve and its surrounding structures, the puncture needle's advancement and local anesthetics spread in real time. The key in UGNB is nerve identification. With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently. We established a public data set containing 320 ultrasound images of brachial plexus (BP). Three experienced doctors jointly produced the BP segmentation ground truth and labeled brachial plexus trunks. We designed a brachial plexus segmentation system (BPSegSys) based on deep learning. BPSegSys achieves experienced-doctor-level nerve identification performance in various experiments. We evaluated BPSegSys performance in terms of intersection-over-union (IoU). Considering three data set groups in our established public data set, the IoUs of BPSegSys were 0.5350, 0.4763 and 0.5043, respectively, which exceed the IoUs 0.5205, 0.4704 and 0.4979 of experienced doctors. In addition, we determined that BPSegSys can help doctors identify brachial plexus trunks more accurately, with IoU improvement up to 27%, which has significant clinical application value. We establish a data set for brachial plexus trunk identification and designed a BPSegSys to identify the brachial plexus trunks. BPSegSys achieves the doctor-level identification of the brachial plexus trunks and improves the accuracy and efficiency of doctors' identification of the brachial plexus trunks.