Abstract
The images automatic segmentation is an important technique for medical treatment. It can help doctor to relieve from heavy works of reading ultrasound images, especially for brachial plexus images. Moreover, the deep learning technology assists doctors in locating the catheters and improves the efficiency and accuracy of injection. However, the research of brachial plexus ultrasonic image segmentation is too few to satisfy the needs of medical application. In this paper, we used a novel modified SegNet to accurately segment brachial plexus. In the training stage, the original training set was divided into two parts randomly (training set 90% and validation set 10%), and the parameters of models were determined and optimized by adopting cross-validation method and data augmentation which can avoid over-fitting effectively. Computational results show that, the model significantly increases nerve segmentation accuracy with 96%; meanwhile, the model is scored 0.644 by Kaggle competition (The Kaggle competition uses CSV files containing the final results for scoring. Besides, the Kaggle competition does not require participants to provide open source code, and all participants’ competition scores and rankings can be found on the website: https://www.kaggle.com/c/ultrasound-nerve-segmentation/leaderboard).
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Notes
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We did not change its decoding format, but modified its up-sampling index number in our model because of the different sizes with the original types.
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This image is a clip of the original dynamic images that we downloaded from: https://www.kaggle.com/chefele/ultrasound-nerve-segmentation/animated-images-with-outlined-nerve-area/code.(the red thin bounding line is the post-production mark.).
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Yan, S., Chen, X., Tang, X., Chi, X. (2023). Segmentation of Brachial Plexus Ultrasound Images Based on Modified SegNet Model. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_31
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