Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN
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Ultrasound (US) provides real-time, two-/three-dimensional safe imaging. Due to these capabilities, it is considered a safe alternative to intra-operative fluoroscopy in various computer-assisted orthopedic surgery (CAOS) procedures. However, interpretation of the collected bone US data is difficult due to high levels of noise, various imaging artifacts, and bone surfaces response appearing several millimeters (mm) in thickness. For US-guided CAOS procedures, it is an essential objective to have a segmentation mechanism, that is both robust and computationally inexpensive.
In this paper, we present our development of a convolutional neural network-based technique for segmentation of bone surfaces from in vivo US scans. The novelty of our proposed design is that it utilizes fusion of feature maps and employs multi-modal images to abate sensitivity to variations caused by imaging artifacts and low intensity bone boundaries. B-mode US images, and their corresponding local phase filtered images are used as multi-modal inputs for the proposed fusion network. Different fusion architectures are investigated for fusing the B-mode US image and the local phase features.
The proposed methods was quantitatively and qualitatively evaluated on 546 in vivo scans by scanning 14 healthy subjects. We achieved an average F-score above 95% with an average bone surface localization error of 0.2 mm. The reported results are statistically significant compared to state-of-the-art.
Reported accurate and robust segmentation results make the proposed method promising in CAOS applications. Further extensive validations are required in order to fully understand the clinical utility of the proposed method.
KeywordsOrthopedic surgery Segmentation Ultrasound Bone Deep learning
This work was supported in part by 2017 North American Spine Society Young Investigator Award.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
- 3.Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1933–1941Google Scholar
- 5.Hacihaliloglu I (2017) Localization of bone surfaces from ultrasound data using local phase information and signal transmission maps. In: International workshop and challenge on computational methods and clinical applications in musculoskeletal imaging. Springer, pp 1–11Google Scholar
- 9.Hazirbas C, Ma L, Domokos C, Cremers D (2016) Fusenet: incorporating depth into semantic segmentation via fusion-based CNN architecture. In: Asian conference on computer vision. Springer, pp 213–228Google Scholar
- 10.Jain V, Bollmann B, Richardson M, Berger DR, Helmstaedter MN, Briggman KL, Denk W, Bowden JB, Mendenhall JM, Abraham WC et al (2010) Boundary learning by optimization with topological constraints. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2488–2495Google Scholar
- 11.Laina I, Rupprecht C, Belagiannis V, Tombari F, Navab N (2016) Deeper depth prediction with fully convolutional residual networks. In: 2016 Fourth international conference on 3D vision (3DV), pp 239–248Google Scholar
- 12.Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440Google Scholar
- 13.Organization WH (2003) The burden of musculoskeletal conditions at the start of the new millennium: report of a who scientific group. WHO Technical Report Series 919Google Scholar
- 15.Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241Google Scholar
- 16.Salehi M, Prevost R, Moctezuma JL, Navab N, Wein W (2017) Precise ultrasound bone registration with learning-based segmentation and speed of sound calibration. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 682–690Google Scholar
- 17.United States Bone and Joint Initiative (2014) The burden of musculoskeletal diseases in the United States (BMUS), 3rd edn. Rosemont, IL. http://www.boneandjointburden.org. Accessed on 13March 2018
- 18.Valada A, Vertens J, Dhall A, Burgard W (2017) Adapnet: Adaptive semantic segmentation in adverse environmental conditions. In: 2017 IEEE International conference on robotics and automation (ICRA). IEEE, pp 4644–4651Google Scholar
- 20.Wang P, Patel VM, Hacihaliloglu I (2018) Simultaneous segmentation and classification of bone surfaces from ultrasound using a multi-feature guided CNN. In: Medical image computing and computer assisted interventionGoogle Scholar