Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection
- 497 Downloads
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.
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.
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).
Our result suggests that the proposed adaptation technique is successful in reducing the divergence between TeUS RF and B-mode data.
KeywordsTemporal enhanced ultrasound Radiofrequency signal B-mode Deep learning Deep belief network Transfer learning Cancer diagnosis Prostate cancer
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).
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.
Informed consent was obtained from all individual participants included in the study.
- 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 653–661Google Scholar
- 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 70–77Google Scholar
- 4.Bengio Y (2012) Deep learning of representations for unsupervised and transfer learning. Unsuperv Transf Learn Chall Mach Learn 7:19Google 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):1019–1024CrossRefPubMedPubMedCentralGoogle 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 331–339Google Scholar
- 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 2960–2967Google Scholar
- 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 513–520Google Scholar
- 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 2066–2073Google 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):727–735CrossRefPubMedGoogle Scholar
- 16.Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google 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–86,701IGoogle 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 806–813Google Scholar
- 25.Seabra J, Sanches JM (2012) RF ultrasound estimation from b-mode images. In: Ultrasound imaging. Springer, US, pp 3–24Google Scholar
- 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 99:1–1Google 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 1526–1531Google Scholar
- 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):1294–1305CrossRefPubMedGoogle 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. IntellGoogle Scholar