Skip to main content
SpringerLink
Log in

Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI

  • Chapter
  • First Online:
Book cover Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

  • 2727 Accesses

Abstract

We present a detection model that is capable of accelerating the inference time of lesion detection from breast dynamically contrast-enhanced magnetic resonance images (DCE-MRI) at state-of-the-art accuracy. In contrast to previous methods based on computationally expensive exhaustive search strategies, our method reduces the inference time with a search approach that gradually focuses on lesions by progressively transforming a bounding volume until the lesion is detected. Such detection model is trained with reinforcement learning and is modeled by a deep Q-network (DQN) that iteratively outputs the next transformation to the current bounding volume. We evaluate our proposed approach in a breast MRI data set containing the T1-weighted and the first DCE-MRI subtraction volume from 117 patients and a total of 142 lesions. Results show that our proposed reinforcement learning based detection model reaches a true positive rate (TPR) of 0.8 at around three false positive detections and a speedup of at least 1.78 times compared to baselines methods.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69(1):7–34

    Article  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67(1):7–30

    Article  Google Scholar 

  3. Siegel RL, Miller KD, Jemal A (2016) Cancer statistics, 2016. CA: Cancer J Clin 66(1):7–30

    Google Scholar 

  4. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424

    Google Scholar 

  5. Smith RA, DeSantis CE (2018) Breast cancer epidemiology. Breast imaging

    Google Scholar 

  6. Lauby-Secretan B, Scoccianti C, Loomis D, Benbrahim-Tallaa L, Bouvard V, Bianchini F, Straif K (2015) Breast cancer screening-viewpoint of the IARC working group. N Engl J Med 372(24):2353–2358

    Article  Google Scholar 

  7. Carter CL, Allen C, Henson DE (1989) Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer 63(1):181–187

    Article  Google Scholar 

  8. Park JH, Anderson WF, Gail MH (2015) Improvements in US breast cancer survival and proportion explained by tumor size and estrogen-receptor status. J Clin Oncol 33(26):2870

    Article  Google Scholar 

  9. Siu AL (2016) Screening for breast cancer: US preventive services task force recommendation statement. Ann Intern Med 164(4):279–296

    Article  Google Scholar 

  10. Kuhl CK, Strobel K, Bieling H, Leutner C, Schild HH, Schrading S (2017) Supplemental breast MR imaging screening of women with average risk of breast cancer. Radiology 283(2):361–370

    Article  Google Scholar 

  11. Weigel S, Heindel W, Heidrich J, Hense HW, Heidinger O (2017) Digital mammography screening: sensitivity of the programme dependent on breast density. Eur Radiol 27(7):2744–2751

    Article  Google Scholar 

  12. Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, Morris E, Pisano E, Schnall M, Sener S et al (2007) American cancer society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 57(2):75–89

    Article  Google Scholar 

  13. Mainiero MB, Moy L, Baron P, Didwania AD, Green ED, Heller SL, Holbrook AI, Lee SJ, Lewin AA, Lourenco AP et al (2017) ACR appropriateness criteria® breast cancer screening. J Am CollE Radiol 14(11):S383–S390

    Article  Google Scholar 

  14. Seely J, Alhassan T (2018) Screening for breast cancer in 2018-what should we be doing today? Curr Oncol 25(Suppl 1):S115

    Article  Google Scholar 

  15. Grimm LJ, Anderson AL, Baker JA, Johnson KS, Walsh R, Yoon SC, Ghate SV (2015) Interobserver variability between breast imagers using the fifth edition of the BI-RADS MRI lexicon. Am J Roentgenol 204(5):1120–1124

    Article  Google Scholar 

  16. Yamaguchi K, Schacht D, Newstead GM, Bradbury AR, Verp MS, Olopade OI, Abe H (2013) Breast cancer detected on an incident (second or subsequent) round of screening MRI: MRI features of false-negative cases. Am J Roentgenol 201(5):1155–1163

    Article  Google Scholar 

  17. Vreemann S, Gubern-Merida A, Lardenoije S, Bult P, Karssemeijer N, Pinker K, Mann R (2018) The frequency of missed breast cancers in women participating in a high-risk MRI screening program. Breast Cancer Res Treat 169(2):323–331

    Article  Google Scholar 

  18. Meeuwis C, van de Ven SM, Stapper G, Gallardo AMF, van den Bosch MA, Willem PTM, Veldhuis WB (2010) Computer-aided detection (CAD) for breast MRI: evaluation of efficacy at 3.0 T. Eur Radiol 20(3):522–528

    Article  Google Scholar 

  19. Maicas G, Bradley AP, Nascimento JC, Reid I, Carneiro G (2018) Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI. arXiv:1809.09404

  20. Vignati A, Giannini V, De Luca M, Morra L, Persano D, Carbonaro LA, Bertotto I, Martincich L, Regge D, Bert A et al (2011) Performance of a fully automatic lesion detection system for breast DCE-MRI. J Magn Reson Imaging 34(6):1341–1351

    Article  Google Scholar 

  21. Gubern-Mérida A, Martí R, Melendez J, Hauth JL, Mann RM, Karssemeijer N, Platel B (2015) Automated localization of breast cancer in DCE-MRI. Med Image Anal 20(1):265–274

    Article  Google Scholar 

  22. McClymont D, Mehnert A, Trakic A, Kennedy D, Crozier S (2014) Fully automatic lesion segmentation in breast MRI using mean-shift and graph-cuts on a region adjacency graph. J Magn Reson Imaging 39(4):795–804

    Article  Google Scholar 

  23. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

    Google Scholar 

  24. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision, pp 21–37. Springer

    Google Scholar 

  25. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788

    Google Scholar 

  26. Ribli D, Horváth A, Unger Z, Pollner P, Csabai I (2018) Detecting and classifying lesions in mammograms with deep learning. Sci Rep 8(1):4165

    Article  Google Scholar 

  27. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529

    Article  Google Scholar 

  28. Renz DM, Böttcher J, Diekmann F, Poellinger A, Maurer MH, Pfeil A, Streitparth F, Collettini F, Bick U, Hamm B et al (2012) Detection and classification of contrast-enhancing masses by a fully automatic computer-assisted diagnosis system for breast MRI. J Magn Reson Imaging 35(5):1077–1088

    Article  Google Scholar 

  29. Amit G, Hadad O, Alpert S, Tlusty T, Gur Y, Ben-Ari R, Hashoul S (2017) Hybrid mass detection in breast MRI combining unsupervised saliency analysis and deep learning. In: International conference on medical image computing and computer-assisted intervention, pp 594–602. Springer

    Google Scholar 

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

    Google Scholar 

  31. Maicas G, Carneiro G, Bradley AP (2017) Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), pp 305–309. IEEE

    Google Scholar 

  32. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

    Google Scholar 

  33. 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, pp 234–241. Springer

    Google Scholar 

  34. Dalmış MU, Vreemann S, Kooi T, Mann RM, Karssemeijer N, Gubern-Mérida A (2018) Fully automated detection of breast cancer in screening MRI using convolutional neural networks. J Med Imaging 5(1):014502

    Article  Google Scholar 

  35. Caicedo JC, Lazebnik S (2015) Active object localization with deep reinforcement learning. In: Proceedings of the IEEE international conference on computer vision, pp 2488–2496

    Google Scholar 

  36. Ghesu FC, Georgescu B, Mansi T, Neumann D, Hornegger J, Comaniciu D (2016) An artificial agent for anatomical landmark detection in medical images. In: International conference on medical image computing and computer-assisted intervention, pp 229–237. Springer

    Google Scholar 

  37. Mann RM, Kuhl CK, Moy L (2019) Contrast-enhanced MRI for breast cancer screening. J Magn Reson Imaging

    Google Scholar 

  38. van Zelst JC, Vreemann S, Witt HJ, Gubern-Merida A, Dorrius MD, Duvivier K, Lardenoije-Broker S, Lobbes MB, Loo C, Veldhuis W et al (2018) Multireader study on the diagnostic accuracy of ultrafast breast magnetic resonance imaging for breast cancer screening. Investig Radiol 53(10):579–586

    Article  Google Scholar 

  39. Sutton RS, Barto AG (1998) Introduction to reinforcement learning, vol 135. MIT press, Cambridge (1998)

    Google Scholar 

  40. Hayton P, Brady M, Tarassenko L, Moore N (1997) Analysis of dynamic MR breast images using a model of contrast enhancement. Med Image Anal 1(3):207–224

    Article  Google Scholar 

  41. Dhungel N, Carneiro G, Bradley AP (2015) Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International conference on digital image computing: techniques and applications (DICTA), pp 1–8. IEEE

    Google Scholar 

  42. Huang G, Sun Y, Liu Z, Sedra D, Weinberger KQ (2016) Deep networks with stochastic depth. In: European conference on computer vision, pp 646–661. Springer

    Google Scholar 

  43. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980

Download references

Acknowledgements

This work was partially supported by the Australian Research Council project (DP180103232). IR acknowledges the Australian Research Council: ARC Centre for Robotic Vision (CE140100016) and Laureate Fellowship (FL130100102). We would like to thank Nvidia for the donation of a Titan Xp used in the development of this work.

Author information

Authors and Affiliations

  1. School of Computer Science, Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia

    Gabriel Maicas, Ian Reid & Gustavo Carneiro

  2. Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, Australia

    Andrew P. Bradley

  3. Institute for Systems and Robotics, Instituto Superior Tecnico, Lisbon, Portugal

    Jacinto C. Nascimento

Authors
  1. Gabriel Maicas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew P. Bradley
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jacinto C. Nascimento
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ian Reid
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gustavo Carneiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gabriel Maicas .

Editor information

Editors and Affiliations

  1. Bethesda Research Lab, PAII Inc., Bethesda, MD, USA

    Le Lu

  2. Nvidia Corporation, Bethesda, MD, USA

    Xiaosong Wang

  3. School of Computer Science, University of Adelaide, Adelaide, SA, Australia

    Gustavo Carneiro

  4. Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

    Lin Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Maicas, G., Bradley, A.P., Nascimento, J.C., Reid, I., Carneiro, G. (2019). Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI. In: Lu, L., Wang, X., Carneiro, G., Yang, L. (eds) Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-13969-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-13969-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13968-1

  • Online ISBN: 978-3-030-13969-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation