TY - JOUR
T1 - BPSegSys
T2 - A Brachial Plexus Nerve Trunk Segmentation System Using Deep Learning
AU - Wang, Yu
AU - Zhu, Binbin
AU - Kong, Lingsi
AU - Wang, Jianlin
AU - Gao, Bin
AU - Wang, Jianhua
AU - Tian, Dingcheng
AU - Yao, Yudong
N1 - Publisher Copyright:
© 2023 World Federation for Ultrasound in Medicine & Biology
PY - 2024/3
Y1 - 2024/3
N2 - 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.
AB - 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.
KW - Brachial plexus
KW - Brachial plexus trunk
KW - Deep learning
KW - Nerve block
KW - Nerve identification
KW - Nerve segmentation
KW - Regional block
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UR - http://www.scopus.com/inward/citedby.url?scp=85181879598&partnerID=8YFLogxK
U2 - 10.1016/j.ultrasmedbio.2023.11.009
DO - 10.1016/j.ultrasmedbio.2023.11.009
M3 - Article
C2 - 38176984
AN - SCOPUS:85181879598
SN - 0301-5629
VL - 50
SP - 374
EP - 383
JO - Ultrasound in Medicine and Biology
JF - Ultrasound in Medicine and Biology
IS - 3
ER -