Chanyan Huang1, Ying Zhou2, Wulin Tan1, Zeting Qiu3, Huaqiang Zhou4, Yiyan Song5, Yue Zhao6, Shaowei Gao1. 1. Department of Anesthesia, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China. 2. Department of Anesthesia, The Third People's Hospital of Chengdu, Chengdu 610031, China. 3. Department of Anesthesia, The First Affiliated Hospital of Shantou University Medical College, Shantou 515041, China. 4. Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510080, China. 5. Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China. 6. Department of General Surgery, Guangdong Second Provincial General Hospital, Guangzhou 510310, China.
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. 2019 Annals of Translational Medicine. All rights reserved.
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. 2019 Annals of Translational Medicine. All rights reserved.
Entities:
Keywords:
Deep learning; U-net; femoral nerve; semantic segmentation; ultrasound
Authors: James Lloyd; Robert Morse; Alasdair Taylor; David Phillips; Helen Higham; David Burckett-St Laurent; James Bowness Journal: Adv Exp Med Biol Date: 2022 Impact factor: 2.622