Original Article
Applying deep learning in recognizing the femoral nerve block region on ultrasound images
Abstract
Background: Identifying the nerve block region is important for the less experienced operators who are not skilled in ultrasound technology. Therefore, we constructed and shared a dataset of ultrasonic images to explore a method to identify the femoral nerve block region.
Methods: Ultrasound images of femoral nerve block were retrospectively collected and marked to establish the dataset. The U-net framework was used for training data and output segmentation of region of interest. The performance of the model was evaluated by Intersection over Union and accuracy. Then the predicted masks were highlighted on the original image to give an intuitive evaluation. Finally, cross validation was used for the whole data to test the robust of the results.
Results: We selected 562 ultrasound images as the whole dataset. The training set intersection over union (IoU) was 0.713, the development set IoU is 0.633 and the test set IoU is 0.638. For the single image, the median and upper/lower quartiles of IoU were 0.722 (0.647–0.789), 0.653 (0.586–0.703), 0.644 (0.555–0.735) for the training set, development set and test set respectively. The segmentation accuracy of the test set was 83.9%. For 10-fold cross validation, the median and quartiles of the 10-iteration sum IoUs was 0.656 (0.628–0.672); for accuracy, they were 88.4% (82.1–90.7%).
Conclusions: We provided a dataset and trained a model for femoral-nerve region segmentation with U-net, obtaining a satisfactory performance. This technique may have potential clinical application.
Methods: Ultrasound images of femoral nerve block were retrospectively collected and marked to establish the dataset. The U-net framework was used for training data and output segmentation of region of interest. The performance of the model was evaluated by Intersection over Union and accuracy. Then the predicted masks were highlighted on the original image to give an intuitive evaluation. Finally, cross validation was used for the whole data to test the robust of the results.
Results: We selected 562 ultrasound images as the whole dataset. The training set intersection over union (IoU) was 0.713, the development set IoU is 0.633 and the test set IoU is 0.638. For the single image, the median and upper/lower quartiles of IoU were 0.722 (0.647–0.789), 0.653 (0.586–0.703), 0.644 (0.555–0.735) for the training set, development set and test set respectively. The segmentation accuracy of the test set was 83.9%. For 10-fold cross validation, the median and quartiles of the 10-iteration sum IoUs was 0.656 (0.628–0.672); for accuracy, they were 88.4% (82.1–90.7%).
Conclusions: We provided a dataset and trained a model for femoral-nerve region segmentation with U-net, obtaining a satisfactory performance. This technique may have potential clinical application.
