Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation
This work addresses the need for accurate and efficient nerve identification in clinical anesthesia, offering a tool to assist doctors, though it is incremental as it applies existing deep learning methods to a new medical imaging dataset.
The paper tackled the problem of automatic nerve identification in ultrasound-guided nerve block anesthesia by developing a deep learning system (BPSegSys) for brachial plexus segmentation, which achieved intersection-over-union (IoU) scores of 0.5238, 0.4715, and 0.5029, exceeding experienced doctors' IoU of 0.5205, 0.4704, and 0.4979, and improved accuracy by up to 27% in trunk identification.
Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthesia method that can 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. Here, we establish a public dataset containing 320 ultrasound images of brachial plexus (BP). Three experienced doctors jointly produce the BP segmentation ground truth and label brachial plexus trunks. We design a brachial plexus segmentation system (BPSegSys) based on deep learning. BPSegSys achieves experienced-doctor-level nerve identification performance in various experiments. We evaluate BPSegSys' performance in terms of intersection-over-union (IoU), a commonly used performance measure for segmentation experiments. Considering three dataset groups in our established public dataset, the IoU of BPSegSys are 0.5238, 0.4715, and 0.5029, respectively, which exceed the IoU 0.5205, 0.4704, and 0.4979 of experienced doctors. In addition, we show that BPSegSys can help doctors identify brachial plexus trunks more accurately, with IoU improvement up to 27%, which has significant clinical application value.