Skip to main content
Erschienen in: International Journal of Computer Assisted Radiology and Surgery 5/2019

13.03.2019 | Original Article

Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN

verfasst von: Ahmed Z. Alsinan, Vishal M. Patel, Ilker Hacihaliloglu

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 5/2019

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Purpose

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.

Method

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.

Results

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.

Conclusions

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Baka N, Leenstra S, van Walsum T (2017) Ultrasound aided vertebral level localization for lumbar surgery. IEEE Trans Med Imaging 36(10):2138–2147CrossRefPubMed Baka N, Leenstra S, van Walsum T (2017) Ultrasound aided vertebral level localization for lumbar surgery. IEEE Trans Med Imaging 36(10):2138–2147CrossRefPubMed
2.
Zurück zum Zitat Cernazanu-Glavan C, Holban S (2013) Segmentation of bone structure in x-ray images using convolutional neural network. Adv Electr Comput Eng 13(1):87–94CrossRef Cernazanu-Glavan C, Holban S (2013) Segmentation of bone structure in x-ray images using convolutional neural network. Adv Electr Comput Eng 13(1):87–94CrossRef
3.
Zurück zum Zitat 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 1933–1941 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 1933–1941
4.
Zurück zum Zitat Hacihaliloglu I (2017) Enhancement of bone shadow region using local phase-based ultrasound transmission maps. Int J Comput Assist Radiol Surg 12(6):951–960CrossRefPubMed Hacihaliloglu I (2017) Enhancement of bone shadow region using local phase-based ultrasound transmission maps. Int J Comput Assist Radiol Surg 12(6):951–960CrossRefPubMed
5.
Zurück zum Zitat 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 1–11 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 1–11
7.
Zurück zum Zitat 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):461–473CrossRef 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):461–473CrossRef
8.
Zurück zum Zitat Hacihaliloglu I, Rasoulian A, Rohling RN, Abolmaesumi P (2014) Local phase tensor features for 3-d ultrasound to statistical shape \(+\) pose spine model registration. IEEE Trans Med Imaging 33(11):2167–2179CrossRefPubMed Hacihaliloglu I, Rasoulian A, Rohling RN, Abolmaesumi P (2014) Local phase tensor features for 3-d ultrasound to statistical shape \(+\) pose spine model registration. IEEE Trans Med Imaging 33(11):2167–2179CrossRefPubMed
9.
Zurück zum Zitat 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 213–228 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 213–228
10.
Zurück zum Zitat 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 2488–2495 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 2488–2495
11.
Zurück zum Zitat 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 239–248 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 239–248
12.
Zurück zum Zitat 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 3431–3440 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 3431–3440
13.
Zurück zum Zitat 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 919 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 919
14.
Zurück zum Zitat Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850CrossRef Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850CrossRef
15.
Zurück zum Zitat 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 234–241 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 234–241
16.
Zurück zum Zitat 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 682–690 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 682–690
18.
Zurück zum Zitat 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 4644–4651 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 4644–4651
19.
Zurück zum Zitat 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):1707–1716CrossRefPubMed 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):1707–1716CrossRefPubMed
20.
Zurück zum Zitat 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 intervention 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 intervention
Metadaten
Titel
Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN
verfasst von
Ahmed Z. Alsinan
Vishal M. Patel
Ilker Hacihaliloglu
Publikationsdatum
13.03.2019
Verlag
Springer International Publishing
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 5/2019
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-019-01934-0

Weitere Artikel der Ausgabe 5/2019

International Journal of Computer Assisted Radiology and Surgery 5/2019 Zur Ausgabe