A Study on Ultrasound Nerve Image Segmentation with multi-densely-layer supervision mechanism
Author: 
Shang GaoAuthors Info & Claims
Shang GaoAuthors Info & ClaimsPages 567 - 572
Abstract
Ultrasound Guided Regional Anesthesia (UGRA) is widely used in pain management of surgical procedure, which can reduce the risk of general anesthesia side-effect and nerve trauma complications. Whereas a successful UGRA requires accurate semantic segmentation of nerve ultrasound images. In this paper, several classical semantic segmentation methods will be introduced and tested at first. Then a novel encoderdecoder based segmentation framework is proposed, that is integrated with densely connection, attention mechanism and multi-layer supervision mechanism. Finally, the proposed method is tested with a public ultrasound nerve benchmark dataset. The experiment with varieties of evaluation methods demonstrates promising performance against several other deep learning models.
References
[1]
Fernandez, N., Santander, J., Ceballos, C: Regional Anesthesia. An Alternative to General Anesthesia in the Management of Neonatal Testicular Torsion. In: Urology, 146, pp.219--221. (2020)
[2]
Hafiane, A., Vieyres, P., Delbos, A.: Deep learning with spatiotemporal consistency for never segmentation in ultrasound images. arXiv preprint arXiv: 1706.05870. (2017)
[3]
Vashishtha V., Aju D.: Nerve segmentation in ultrasound images. In: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, 2017, pp. 1--5. (2017)
[4]
Long, J. Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431--3440. (2015)
[5]
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid Scene Parsing Network. arXiv preprint arXiv: 1612.01105(2017)
[6]
Wang, Z. Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT. arXiv preprint arXiv:2103.05546. (2021)
[7]
Chen, L. Papandreou, G. Schroff, F. Adam, H.: Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv preprint arXiv: 1706.0558. (2017)
[8]
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional net- works for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2015, pp. 234--241. (2015)
[9]
Hadjerci, O., Hafiane, Makris, A., P. D. Conte, P. Vieyres, and Delbos, A.: Nerve detection in ultrasound images using median gabor binary pattern. In: International Conference Image Analysis and Recognition. Springer, 2014, pp. 132--140. (2014)
[10]
Baby, M., Jereesh, A.: Automatic nerve segmentation of ultrasound images. In: Electronics, Communication and Aerospace Technology (ICECA), 2017 International conference of, vol. 1. IEEE, 2017, pp. 107--112. (2017)
[11]
Wang, Y., Wei, C., Wang, Z., Lu Q., Wang, C.: A More Streamlined U-net for Nerve Segmentation in Ultrasound Images. In: 2018 Chinese Automation Congress (CAC), 2018, pp. 101--104. (2018)
[12]
Abraham, N., Illanko, K., Khan, N., Androutsos, D.: Deep Learning for Semantic Segmentation of Brachial Plexus Nervesin Ultrasound Images Using U-NetandM-Net. In: 2019 3rd International Conference on Imaging, Signal Processing and Communication. (2019)
[13]
Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. arXiv preprint arXiv:1409.0575. (2014)
[14]
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770--778. (2016)
[15]
Z. Wang, Z. Zhang and I. Voiculescu, "RAR-U-NET: A Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation Under Noisy Labels," 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 21--25. (2021)
[16]
Huang, G., Liu, Z., Weinberger, K., Laurens van der Maaten.: Densely Connected Convolutional Networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4700--4708. (2017)
[17]
Woo, S., Park, J., Lee, J., Kweon, I.: CBAM: Convolutional Block Attention Module. arXiv preprint arXiv: 1807.06521v2. (2018)
[18]
Zhou, Z., Md Mahfuzur Rahman Siddiquee, N. Tajbakhsh and Liang, J.: UNet++: A Nested U-Net Architecture for Medical Image Segmentation. In: Deep Learning in Medical Image Analysis (DLMIA). (2018).
[19]
Kaggle ultrasound nerve segmentation. https://www.kaggle.com/c/ultrasound-nerve-segmentation
Index Terms
- A Study on Ultrasound Nerve Image Segmentation with multi-densely-layer supervision mechanism
Recommendations
Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization
To find the optimum threshold of an image is still an important research topic in the recent years. This paper presents a segmentation of liver cyst for ultrasound image through combining Wellner's thresholding algorithm with particle swarm optimization (...
An integrated method for atherosclerotic carotid plaque segmentation in ultrasound image
We propose an integrated learning-based method to segment atherosclerotic carotid plaque in ultrasound image.Four different classification algorithms, along with the auto-context iterative model, are employed to implement the pixel-wise classification, ...
A comparative study of ultrasound image segmentation algorithms for segmenting kidney tumors
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication TechnologiesIn this paper we introduce an ultrasound image segmentation evaluation framework for kidney tumor. Ultrasound image segmentation algorithms can be divided into edge based, region based, texture based, active contour and model base technique. We tested ...
Comments
Information & Contributors
Information
Published In
October 2021
1723 pages
ISBN:9781450384322
DOI:10.1145/3501409
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 31 December 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
EITCE 2021
EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering
October 22 - 24, 2021
Xiamen, China
Acceptance Rates
EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;
Overall Acceptance Rate 508 of 972 submissions, 52%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 32Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)2
Reflects downloads up to 06 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in