Abstract
Purpose
Real-time, two (2D) and three-dimensional (3D) ultrasound (US) has been investigated as a potential alternative to fluoroscopy imaging in various surgical and non-surgical orthopedic procedures. However, low signal to noise ratio, imaging artifacts and bone surfaces appearing several millimeters (mm) in thickness have hindered the wide spread adaptation of this safe imaging modality. Limited field of view and manual data collection cause additional problems during US-based orthopedic procedures. In order to overcome these limitations various bone segmentation and registration methods have been developed. Acoustic bone shadow is an important image artifact used to identify the presence of bone boundaries in the collected US data. Information about bone shadow region can be used (1) to guide the orthopedic surgeon or clinician to a standardized diagnostic viewing plane with minimal artifacts, (2) as a prior feature to improve bone segmentation and registration.
Method
In this work, we propose a computational method, based on a novel generative adversarial network (GAN) architecture, to segment bone shadow images from in vivo US scans in real-time. We also show how these segmented shadow images can be incorporated, as a proxy, to a multi-feature guided convolutional neural network (CNN) architecture for real-time and accurate bone surface segmentation. Quantitative and qualitative evaluation studies are performed on 1235 scans collected from 27 subjects using two different US machines. Finally, we provide qualitative and quantitative comparison results against state-of-the-art GANs.
Results
We have obtained mean dice coefficient (± standard deviation) of \(93\%\) (\(\pm \,0.02 \)) for bone shadow segmentation, showing that the method is in close range with manual expert annotation. Statistical significant improvements against state-of-the-art GAN methods (paired t-test \(p<0.05\)) is also obtained. Using the segmented bone shadow features average bone localization accuracy of 0.11 mm (\(\pm \,0.16 \)) was achieved.
Conclusions
Reported accurate and robust results make the proposed method promising for various orthopedic procedures. Although we did not investigate in this work, the segmented bone shadow images could also be used as an additional feature to improve accuracy of US-based registration methods. Further extensive validations are required in order to fully understand the clinical utility of the proposed method.
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References
Alsinan AZ, Patel VM, Hacihaliloglu I (2019) Automatic segmentation of bone surfaces from ultrasound using a filter layer guided cnn. Int J Comput Assist Radiol Surg 14(5):775–783
Baka N, Leenstra S, van Walsum T (2017) Ultrasound aided vertebral level localization for lumbar surgery. IEEE Trans Med Imaging 36(10):2138–2147
Berton F, Cheriet F, Miron MC, Laporte C (2016) Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images. Comput Biol Med 72:201–211
Cernazanu-Glavan C, Holban S (2013) Segmentation of bone structure in X-ray images using convolutional neural network. Adv Electr Comput Eng 13(1):87–94
El-Hariri H, Mulpuri K, Hodgson A, Garbi R (2019) Comparative evaluation of hand-engineered and deep-learned features for neonatal hip bone segmentation in ultrasound. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp. 12–20
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing Systems 27. Curran Associates, Inc., pp 2672–2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
Hacihaliloglu I (2017) Enhancement of bone shadow region using local phase-based ultrasound transmission maps. Int J Comput Assist Radiol Surg 12(6):951–960
Hacihaliloglu I (2017) Ultrasound imaging and segmentation of bone surfaces: a review. Technology 05(02):74–80
Hacihaliloglu I, Guy P, Hodgson AJ, Abugharbieh R (2014) Volume-specific parameter optimization of 3d local phase features for improved extraction of bone surfaces in ultrasound. Int J Med Robot Comput Assist Surg 10(4):461–473
Hellier P, Coupé P, Morandi X, Collins DL (2010) An automatic geometrical and statistical method to detect acoustic shadows in intraoperative ultrasound brain images. Med Image Anal 14(2):195–204
Hu R, Singla R, Deeba F, Rohling RN (2019) Acoustic shadow detection: study and statistics of b-mode and radiofrequency data. Ultrasound Med Biol 45(8):2248–2257
Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp. 5967–5976. IEEE
Jain V, Bollmann B, Richardson M, Berger DR, Helmstaedter MN, Briggman KL, Denk W, Bowden JB, Mendenhall JM, Abraham WC, Harris KM, Kasthuri N, Hayworth KJ, Schalek R, Tapia JC, Lichtman JW, Seung SH (2010) Boundary learning by optimization with topological constraints. In: 2010 IEEE Conference on computer vision and pattern recognition (CVPR), pp. 2488–2495. IEEE
Karamalis A, Wein W, Klein T, Navab N (2012) Ultrasound confidence maps using random walks. Med Image Anal 16(6):1101–1112
Klein T, Wells WM (2015) Rf ultrasound distribution-based confidence maps. In: International conference on medical image computing and computer-assisted intervention, vol 9350. Springer, Berlin, pp. 595–602
Laina I, Rupprecht C, Belagiannis V, Tombari F, Navab N (2016) Deeper depth prediction with fully convolutional residual networks. In: 2016 4th international conference on 3D vision (3DV), pp. 239–248. IEEE
Meng Q, Housden J, Matthew J, Rueckert D, Schnabel JA, Kainz B, Sinclair M, Zimmer V, Hou B, Rajchl M, Toussaint N, Oktay O, Schlemper J, Gomez A (2019) Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging. IEEE Trans Med Imaging 38(12):2755–2767
Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: 4th International conference on learning representations, ICLR 2016, San Juan, Puerto Rico, May 2–4, 2016, conference track proceedings. arXiv:1511.06434
Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850
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, Berlin, pp 234–241
Schumann S (2016) State of the art of ultrasound-based registration in computer assisted orthopedic interventions. In: Zheng G, Li S (eds) Computational radiology for orthopaedic interventions. Springer, Berlin, pp 271–297
Villa M, Dardenne G, Nasan M, Letissier H, Hamitouche C, Stindel E (2018) Fcn-based approach for the automatic segmentation of bone surfaces in ultrasound images. Int J Comput Assist Radiol Surg 13(11):1707–1716
Wang P, Patel VM, Hacihaliloglu I (2018) Simultaneous segmentation and classification of bone surfaces from ultrasound using a multi-feature guided cnn. In: International conference on medical image computing and computer-assisted intervention, vol 11073. Springer, Berlin, pp 134–142
Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853
Zheng G, Nolte LP (2015) Computer-assisted orthopedic surgery: current state and future perspective. Front Surg 2:66
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This work was supported in part by 2017 North American Spine Society Young Investigator Award.
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Alsinan, A.Z., Patel, V.M. & Hacihaliloglu, I. Bone shadow segmentation from ultrasound data for orthopedic surgery using GAN. Int J CARS 15, 1477–1485 (2020). https://doi.org/10.1007/s11548-020-02221-z
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DOI: https://doi.org/10.1007/s11548-020-02221-z