Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection | International Journal of Computer Assisted Radiology and Surgery Skip to main content
Springer Nature Link
Log in

Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

We present a method for prostate cancer (PCa) detection using temporal enhanced ultrasound (TeUS) data obtained either from radiofrequency (RF) ultrasound signals or B-mode images.

Methods

For the first time, we demonstrate that by applying domain adaptation and transfer learning methods, a tissue classification model trained on TeUS RF data (source domain) can be deployed for classification using TeUS B-mode data alone (target domain), where both data are obtained on the same ultrasound scanner. This is a critical step for clinical translation of tissue classification techniques that primarily rely on accessing RF data, since this imaging modality is not readily available on all commercial scanners in clinics. Proof of concept is provided for in vivo characterization of PCa using TeUS B-mode data, where different nonlinear processing filters in the pipeline of the RF to B-mode conversion result in a distribution shift between the two domains.

Results

Our in vivo study includes data obtained in MRI-guided targeted procedure for prostate biopsy. We achieve comparable area under the curve using TeUS RF and B-mode data for medium to large cancer tumor sizes in biopsy cores (>4 mm).

Conclusion

Our result suggests that the proposed adaptation technique is successful in reducing the divergence between TeUS RF and B-mode data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Azizi S, Imani F, Ghavidel S, Tahmasebi A, Wood B, Mousavi P, Abolmaesumi P (2016) Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study. Int J Comput Assist Radiol Surg 11:1–10

    Article  Google Scholar 

  2. Azizi S, Imani F, Kwak JT, Tahmasebi A, Xu S, Yan P, Kruecker J, Turkbey B, Choyke P, Pinto P, Wood B, Mousavi P, Abolmaesumi P (2016) Classifying cancer grades using temporal ultrasound for transrectal prostate biopsy. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 653–661

  3. Azizi S, Imani F, Zhuang B, Tahmasebi A, Kwak JT, Xu S, Uniyal N, Turkbey B, Choyke P, Pinto P, Wood B, Mousavi P, Abolmaesumi P (2015) Ultrasound-based detection of prostate cancer using automatic feature selection with deep belief networks. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 70–77

  4. Bengio Y (2012) Deep learning of representations for unsupervised and transfer learning. Unsuperv Transf Learn Chall Mach Learn 7:19

    Google Scholar 

  5. Conjeti S, Katouzian A, Roy AG, Peter L, Sheet D, Carlier S, Laine A, Navab N (2016) Supervised domain adaptation of decision forests: transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization. Med Image Anal 32:1–17

    Article  PubMed  Google Scholar 

  6. Daoud MI, Mousavi P, Imani F, Rohling R, Abolmaesumi P (2013) Tissue classification using ultrasound-induced variations in acoustic backscattering features. IEEE Trans Biomed Eng 60(2):310–320

    Article  PubMed  Google Scholar 

  7. Epstein JI, Feng Z, Trock BJ, Pierorazio PM (2012) Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified gleason grading system and factoring in tertiary grades. Eur Urol 61(5):1019–1024

    Article  PubMed  PubMed Central  Google Scholar 

  8. Feleppa E, Porter C, Ketterling J, Dasgupta S, Ramachandran S, Sparks D (2007) Recent advances in ultrasonic tissue-type imaging of the prostate. In: Acoustical imaging. Springer, Netherlands, pp 331–339

  9. Fernando B, Habrard A, Sebban M, Tuytelaars T (2013) Unsupervised visual domain adaptation using subspace alignment. In: Proceedings of the IEEE international conference on computer vision, pp 2960–2967

  10. Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 513–520

  11. Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: Computer vision and pattern recognition (CVPR), 2012 IEEE conference on, IEEE, pp 2066–2073

  12. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  PubMed  Google Scholar 

  13. Imani F, Abolmaesumi P, Gibson E, Khojaste A, Gaed M, Moussa M, Gomez JA, Romagnoli C, Leveridge M, Chang S (2015) Computer-aided prostate cancer detection using ultrasound rf time series: in vivo feasibility study. IEEE Trans Med Imaging 34(11):2248–2257

    Article  PubMed  Google Scholar 

  14. Imani F, Ramezani M, Nouranian S, Gibson E, Khojaste A, Gaed M, Moussa M, Gomez JA, Romagnoli C, Leveridge M (2015) Ultrasound-based characterization of prostate cancer using joint independent component analysis. IEEE Trans Biomed Eng 62(7):1796–1804

    Article  PubMed  Google Scholar 

  15. Imani F, Zhuang B, Tahmasebi A, Kwak JT, Xu S, Agarwal H, Bharat S, Uniyal N, Turkbey IB, Choyke P, Pinto P (2015) Augmenting mri-transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study. Int J Comput Assist Radiol Surg 10(6):727–735

    Article  PubMed  Google Scholar 

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  17. Mirzaalian H, Ning L, Savadjiev P, Pasternak O, Bouix S, Michailovich O, Grant G, Marx C, Morey R, Flashman L (2016) Inter-site and inter-scanner diffusion MRI data harmonization. NeuroImage 135:311–323

    Article  CAS  PubMed  Google Scholar 

  18. Moradi M, Abolmaesumi P, Mousavi P (2010) Tissue typing using ultrasound RF time series: experiments with animal tissue samples. Med Phys 37(8):4401–4413

    Article  PubMed  Google Scholar 

  19. Moradi M, Abolmaesumi P, Siemens DR, Sauerbrei EE, Boag AH, Mousavi P (2009) Augmenting detection of prostate cancer in transrectal ultrasound images using svm and RF time series. IEEE Trans Biomed Eng 56(9):2214–2224

    Article  PubMed  Google Scholar 

  20. Moradi M, Mahdavi SS, Nir G, Jones EC, Goldenberg SL, Salcudean SE (2013) Ultrasound RF time series for tissue typing: first in vivo clinical results. In: SPIE medical imaging. International society for optics and photonics, pp 86,701I–86,701I

  21. Moradi M, Mousavi P, Abolmaesumi P (2007) Computer-aided diagnosis of prostate cancer with emphasis on ultrasound-based approaches: a review. Ultrasound Medicine Biol 33(7):1010–1028

    Article  Google Scholar 

  22. Oelze ML, O’Brien WD, Blue JP, Zachary JF (2004) Differentiation and characterization of rat mammary fibroadenomas and 4t1 mouse carcinomas using quantitative ultrasound imaging. IEEE Trans Med Imaging 23(6):764–771

    Article  PubMed  Google Scholar 

  23. Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  24. Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 806–813

  25. Seabra J, Sanches JM (2012) RF ultrasound estimation from b-mode images. In: Ultrasound imaging. Springer, US, pp 3–24

  26. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 99:1–1

    Google Scholar 

  27. Tanaka M, Okutomi M (2014) A novel inference of a restricted Boltzmann machine. In: International conference on pattern recognition (ICPR), 2014 22nd, IEEE, pp 1526–1531

  28. van Engelen A, van Dijk AC, Truijman MT, van’t Klooster R, van Opbroek A, van der Lugt A, Niessen WJ, Kooi ME, de Bruijne M (2015) Multi-center MRI carotid plaque component segmentation using feature normalization and transfer learning. IEEE Trans Med Imaging 34(6):1294–1305

    Article  PubMed  Google Scholar 

  29. Van Opbroek A, Ikram MA, Vernooij MW, De Bruijne M (2015) Transfer learning improves supervised image segmentation across imaging protocols. IEEE Trans Med Imaging 34(5):1018–1030

    Article  PubMed  Google Scholar 

  30. Zhuang F, Cheng X, Luo P, Pan SJ, He Q (2015) Supervised representation learning: transfer learning with deep autoencoders. In: Int. Joint Conf. Artif. Intell

Download references

Acknowledgements

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and in part by the Canadian Institutes of Health Research (CIHR).

Author information

Authors and Affiliations

  1. The University of British Columbia, Vancouver, BC, Canada

    Shekoofeh Azizi & Purang Abolmaesumi

  2. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  3. Philips Research North America, Cambridge, MA, USA

    Pingkun Yan & Amir Tahmasebi

  4. Sejong University, Gwangjin-Gu, SU, Korea

    Jin Tae Kwak

  5. National Institutes of Health, Bethesda, MD, USA

    Sheng Xu, Baris Turkbey, Peter Choyke, Peter Pinto & Bradford Wood

Authors
  1. Shekoofeh Azizi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Parvin Mousavi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Pingkun Yan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Amir Tahmasebi
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Jin Tae Kwak
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Sheng Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Baris Turkbey
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Peter Choyke
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Peter Pinto
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Bradford Wood
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Purang Abolmaesumi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Shekoofeh Azizi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Azizi, S., Mousavi, P., Yan, P. et al. Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection. Int J CARS 12, 1111–1121 (2017). https://doi.org/10.1007/s11548-017-1573-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-017-1573-x

Keywords