Automatic Segmentation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intelligence

Abstract

Ultrasound-guided quadratus lumborum block (QLB) technology has become a widely used perioperative analgesia method during abdominal and pelvic surgeries. Due to the anatomical complexity and individual variability of the quadratus lumborum muscle (QLM) on ultrasound images, nerve blocks heavily rely on anesthesiologist experience. Therefore, using artificial intelligence (AI) to identify different tissue regions in ultrasound images is crucial. In our study, we retrospectively collected 112 patients (3162 images) and developed a deep learning model named Q-VUM, which is a U-shaped network based on the Visual Geometry Group 16 (VGG16) network. Q-VUM precisely segments various tissues, including the QLM, the external oblique muscle, the internal oblique muscle, the transversus abdominis muscle (collectively referred to as the EIT), and the bones. Furthermore, we evaluated Q-VUM. Our model demonstrated robust performance, achieving mean intersection over union (mIoU), mean pixel accuracy, dice coefficient, and accuracy values of 0.734, 0.829, 0.841, and 0.944, respectively. The IoU, recall, precision, and dice coefficient achieved for the QLM were 0.711, 0.813, 0.850, and 0.831, respectively. Additionally, the Q-VUM predictions showed that 85% of the pixels in the blocked area fell within the actual blocked area. Finally, our model exhibited stronger segmentation performance than did the common deep learning segmentation networks (0.734 vs. 0.720 and 0.720, respectively). In summary, we proposed a model named Q-VUM that can accurately identify the anatomical structure of the quadratus lumborum in real time. This model aids anesthesiologists in precisely locating the nerve block site, thereby reducing potential complications and enhancing the effectiveness of nerve block procedures.

Keywords: Convolutional neural network; Deep learning; Image segmentation; Quadratus lumborum block; Ultrasonography.

Fig. 1

Flowchart of the overall experimental procedure

Fig. 2

The architecture of the Q-VUM

Fig. 3

The results of the Q-VUM predictions produced for each region

Fig. 4

Significant differences between different groups (Turkey test)

Fig. 5

Visualization of the segmentation results produced in different areas. The original image (a), the manually segmented image (b), and the prediction result (c) of the Q-VUM. In the ultrasound images, green, red, and yellow represent the QLM, EIT, and bones, respectively

Fig. 6

Comparison among the results produced by different models. The original image (a), the corresponding gold standard (b), the prediction result of the Q-VUM (c), the prediction result of UNet (d), the prediction result of UNet +  + (e), the prediction result of BPSegSys (f), and is the prediction of VisTR (g)