Skip to main content
SpringerLink
Log in

Image-Guided Radiotherapy with Machine Learning

  • Chapter
Machine Learning in Radiation Oncology

  • 6647 Accesses

Abstract

In the past decades, many machine learning techniques have been successfully developed and applied to the field of image-guided radiotherapy (IGRT). In this chapter, we will present some latest developments in the application of machine learning techniques to this field. In particular, we focus on the recently developed machine learning methods for delineating male pelvic structures for the treatment of prostate cancer. In the first few sections, we will present and discuss automatic and semiautomatic methods for CT prostate segmentation in the IGRT workflow. In the last section, we will present our extension of some recently developed machine learning approaches to segment the prostate in MR images.

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others

References

  1. Weiss E, Hess CF. The impact of gross tumor volume (GTV) and clinical target volume (CTV) definition on the total accuracy in radiotherapy theoretical aspects and practical experiences. Strahlenther Onkol. 2003;179(1):21–30.

    Article  PubMed  Google Scholar 

  2. Brouwer CL, et al. 3D Variation in delineation of head and neck organs at risk. Radiat Oncol. 2012;7:32.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Sharp G, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014;41(5):050902.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Rohlfing T, et al. Quo vadis, atlas-based segmentation? In: Handbook of biomedical image analysis. USA: Springer; 2005. p. 435–86.

    Google Scholar 

  5. Heimann T, Meinzer HP. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal. 2009;13(4):543–63.

    Article  PubMed  Google Scholar 

  6. Geremia E, et al. Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage. 2011;57(2):378–90.

    Article  PubMed  Google Scholar 

  7. Li W, et al. Learning image context for segmentation of the prostate in CT-guided radiotherapy. Phys Med Biol. 2012;57(5):1283–308.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Criminisi A, Shotton J, Konukoglu E. Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found Trends Comput Graph Vis. 2012;7(2–3):81–227.

    Google Scholar 

  9. Shukla-Dave A, Hricak H. Role of MRI in prostate cancer detection. NMR Biomed. 2014;27(1):16–24.

    Article  PubMed  Google Scholar 

  10. Freedman D, et al. Model-based segmentation of medical imagery by matching distributions. IEEE Trans Med Imaging. 2005;24(3):281–92.

    Article  PubMed  Google Scholar 

  11. Costa MJ, et al. Automatic segmentation of bladder and prostate using coupled 3D deformable models. Med Image Comput Comput Assist Interv. 2007;10(Pt 1):252–60.

    PubMed  Google Scholar 

  12. Foskey M, et al. Large deformation three-dimensional image registration in image-guided radiation therapy. Phys Med Biol. 2005;50(24):5869.

    Article  PubMed  Google Scholar 

  13. Chen S, Lovelock DM, Radke RJ. Segmenting the prostate and rectum in CT imagery using anatomical constraints. Med Image Anal. 2011;15(1):1–11.

    Article  CAS  PubMed  Google Scholar 

  14. Haas B, et al. Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies. Phys Med Biol. 2008;53(6):1751.

    Article  CAS  PubMed  Google Scholar 

  15. Ghosh P, Mitchell M. Segmentation of medical images using a genetic algorithm. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation. Seattle:ACM; 2006. p. 1171–8.

    Google Scholar 

  16. Viola P, Jones MJ. Robust real-time face detection. Int J Comput Vis. 2004;57(2):137–54.

    Article  Google Scholar 

  17. Zhan Y, Dewan M, Harder M, Krishnan A, Zhou XS. Robust automatic knee MR slice positioning through redundant and hierarchical anatomy detection. IEEE Trans Med Imaging. 2011;30(12):2087–100.

    Article  PubMed  Google Scholar 

  18. Zhan Y, Zhou XS, Peng Z, Krishnan A. Active Scheduling of Organ Detection and Segmentation in Whole-Body Medical Images. In: Metaxas D et al., editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. Berlin/Heidelberg: Springer; 2008. p. 313–21.

    Chapter  Google Scholar 

  19. Peng H, Fulmi L, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27(8):1226–38.

    Article  PubMed  Google Scholar 

  20. Zhang S, Zhan Y, Metaxas DN. Deformable segmentation via sparse representation and dictionary learning. Med Image Anal. 2012;16(7):1385–96.

    Article  PubMed  Google Scholar 

  21. Gao Y, Zhang Y, Shen D. Incremental learning with selective memory (ILSM): towards fast prostate localization for image guided radiotherapy. IEEE Trans Med Imaging. 2014;33(2):518–34.

    Article  PubMed Central  PubMed  Google Scholar 

  22. Davis BC, et al. Automatic segmentation of intra-treatment CT images for adaptive radiation therapy of the prostate. Med Image Comput Comput Assist Interv. 2005;8(Pt 1):442–50.

    CAS  PubMed  Google Scholar 

  23. Garrigues P, Olshausen B. Group sparse coding with a laplacian scale mixture prior. Adv Neural Inf Process Syst. 2010;23:1–9.

    Google Scholar 

  24. Krause A, Cevher V. Submodular dictionary selection for sparse representation. In: ICML 2010: proceedings of the 27th international conference on Machine learning. Haifa: Omnipress; 2010.

    Google Scholar 

  25. Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process. 2006;54(11):4311–22.

    Article  Google Scholar 

  26. Huang J, Yang M. Fast sparse representation with prototypes. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE conference on. San Francisco, CA; 2010.

    Google Scholar 

  27. Jiang Z, Lin Z, Davis LS. Learning a discriminative dictionary for sparse coding via label consistent K-SVD. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE conference on. Providence, RI; 2011.

    Google Scholar 

  28. Baraniuk R, et al. Applications of sparse representation and compressive sensing. Proc IEEE. 2010;98(6):906–9.

    Article  Google Scholar 

  29. Wright J, et al. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell. 2009;31(2):210–27.

    Article  PubMed  Google Scholar 

  30. Elisseeff IGA. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–82.

    Google Scholar 

  31. Zou H, Hastie T. Regularization and variable selection via the Elastic Net. J Royal Stat Soc B. 2005;67:301–20.

    Article  Google Scholar 

  32. Tu Z, Bai X. Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans Pattern Anal Mach Intell. 2010;32(10):1744–57.

    Article  PubMed  Google Scholar 

  33. Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297–302.

    Article  Google Scholar 

  34. Warfield SK, Zou KH, Wells WM. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004;23(7):903–21.

    Article  PubMed Central  PubMed  Google Scholar 

  35. Coupé P, et al. Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage. 2011;54(2):940–54.

    Article  PubMed  Google Scholar 

  36. Rousseau F, Habas PA, Studholme C. A supervised patch-based approach for human brain labeling. IEEE Trans Med Imaging. 2011;30(10):1852–62.

    Article  PubMed Central  PubMed  Google Scholar 

  37. Liao S, Shen D. A learning based hierarchical framework for automatic prostate localization in CT images. In: Madabhushi A et al., editors. Prostate cancer imaging. Image analysis and image-guided interventions. Berlin/Heidelberg: Springer; 2011. p. 1–9.

    Chapter  Google Scholar 

  38. Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell. 1989;11(7):674–93.

    Article  Google Scholar 

  39. Dalal N, Triggs B. Histograms of oriented gradients for human detection. 2005.

    Book  Google Scholar 

  40. Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002;24(7):971–87.

    Article  Google Scholar 

  41. Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J Royal Stat Soc B Stat Methodol. 2011;73(3):273–82.

    Article  Google Scholar 

  42. Belkin M, Niyogi P, Sindhwani V. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res. 2006;7:2399–434.

    Google Scholar 

  43. Shi Y, et al. Transductive prostate segmentation for CT image guided radiotherapy. In: Wang F et al., editors. Machine learning in medical imaging. Berlin/Heidelberg: Springer; 2012. p. 1–9.

    Chapter  Google Scholar 

  44. Tibshirani R, et al. Sparsity and smoothness via the fused lasso. J Royal Stat Soc B Stat Methodol. 2005;67(1):91–108.

    Article  Google Scholar 

  45. Feng Q, et al. Segmenting CT prostate images using population and patient-specific statistics for radiotherapy. In: Proceedings of the sixth IEEE international conference on symposium on biomedical imaging: From Nano to Macro. Boston: IEEE Press; 2009. p. 282–5.

    Google Scholar 

  46. Jain A, Zongker D. Feature selection: evaluation, application, and small sample performance. IEEE Transactions Pattern Anal Mach Intell. 1997;19(2):153–8.

    Article  Google Scholar 

  47. Bühlmann P. Bagging, boosting and ensemble methods. In: Gentle JE, Härdle WK, Mori Y, editors. Handbook of computational statistics. Berlin/Heidelberg: Springer; 2012. p. 985–1022.

    Chapter  Google Scholar 

  48. Zhang S, et al. Towards robust and effective shape modeling: sparse shape composition. Med Image Anal. 2012;16(1):265–77.

    Article  PubMed  Google Scholar 

  49. Shen D, Ip HHS. A Hopfield neural network for adaptive image segmentation: an active surface paradigm. Pattern Recognit Lett. 1997;18(1):37–48.

    Article  Google Scholar 

  50. Liao S, et al. Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization. In: Gee J et al., editors. Information processing in medical imaging. Berlin/Heidelberg: Springer; 2013. p. 511–23.

    Chapter  Google Scholar 

  51. Liao S, et al. Representation learning: a unified deep learning framework for automatic prostate MR segmentation. In: Mori K et al., editors. Medical image computing and computer-assisted intervention – MICCAI 2013. Berlin/Heidelberg: Springer; 2013. p. 254–61.

    Chapter  Google Scholar 

  52. Kirschner M, Jung F, Wesarg S. Automatic prostate segmentation in MR images with a probabilistic active shape model. In: PRostate MR Image SEgmentation, PROMISE 2012. Nice: Electronic Publication; 2012. p. 28–35.

    Google Scholar 

  53. Maan B, van der Heijden F. Prostate MR image segmentation using 3D active appearance models. In: PRostate MR Image SEgmentation, PROMISE 2012. Nice: Electronic Publication; 2012. p. 44–51.

    Google Scholar 

  54. Martin S, Troccaz J, Daanen V. Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model. Med Phys. 2010;37(4):1579–90.

    Article  PubMed  Google Scholar 

  55. Birkbeck N, Zhang J, Zhou SK. Region-specific hierarchical segmentation of MR prostate using discriminative learning. In: The PRostate MR Image SEgmentation, PROMISE 2012. Nice: Electronic Publication; 2012.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UNC IDEA Group, Department of Radiology, Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, 27599, USA

    Yaozong Gao, Yanrong Guo & Dinggang Shen

  2. State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, Jiangsu, China, 210023

    Yinghuan Shi

  3. Syngo, Siemens Medical Solutions, Malvern, PA, 19355, USA

    Shu Liao

  4. Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC, 27599, USA

    Jun Lian

  5. Department of Radiology, University of North Carolina, 124 Mason Farm Road, Chapel Hill, NC, 27599, USA

    Dinggang Shen

Authors
  1. Yaozong Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanrong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yinghuan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shu Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jun Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dinggang Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dinggang Shen .

Editor information

Editors and Affiliations

  1. Department of Oncology, McGill University, Montreal, Canada

    Issam El Naqa

  2. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA

    Ruijiang Li

  3. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA

    Martin J. Murphy

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Gao, Y., Guo, Y., Shi, Y., Liao, S., Lian, J., Shen, D. (2015). Image-Guided Radiotherapy with Machine Learning. In: El Naqa, I., Li, R., Murphy, M. (eds) Machine Learning in Radiation Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-18305-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18305-3_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18304-6

  • Online ISBN: 978-3-319-18305-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation