Skip to main content
SpringerLink
Log in

Evaluating the Impact of Training Loss on MR to Synthetic CT Conversion

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12565))

Abstract

Radiation therapy is one of the most important strategies for treating patients with tumor. The rationale is to deliver high radiation doses to the tumor in order to damage its DNA while sparing, at the same time, healthy tissues. In order to optimize such a process, biomedical images play a fundamental role; in particular, Magnetic Resonance (MR) produces well-contrasted images for precisely contouring tumors and organs at risk. However, due to the physical information stored in it, Computed Tomography (CT) is mandatory for accurate radiation dose calculation. To overcome this limitation, several algorithms, usually based on deep learning techniques, were proposed for converting MR–to–synthetic CT (sCT). In this paper, we report about the evaluation of the impact of three different train losses, commonly used for non–medical applications. Tests were ran on a cohort of 15 brain MR/CT image pairs. An algorithm for MR–to–sCT conversion, previously developed for MR-only radiotherapy, was used as benchmark platform. Predicted sCT images were compared on the basis of intensities and edges reconstruction. Results show that potential improvements can be achieved if non–medical image loss will be adapted to this application.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Notes

  1. 1.

    Two types of MR acquisitions that, by taking into account different matter properties, lead to different contrast among tissues.

References

  1. Yang, X., et al.: MRI-based proton radiotherapy for prostate cancer using deep convolutional neural networks. Int. J. Radiat. Oncol. Biol. Phys. 105, S200 (2019)

    Article  Google Scholar 

  2. Sjölund, J., Forsberg, D., Andersson, M., Knutsson, H.: Generating patient specific pseudo-CT of the head from MR using atlas-based regression. Phys. Med. Biol. 60, 825–839 (2015)

    Article  Google Scholar 

  3. Dowling, J.A., et al.: Automatic substitute computed tomography generation and contouring for magnetic resonance imaging (MRI)-alone external beam radiation therapy from standard MRI sequences. Int. J. Radiat. Oncol. Biol. Phys. 93, 1144–1153 (2015)

    Article  Google Scholar 

  4. Arabi, H., Koutsouvelis, N., Rouzaud, M., Miralbell, R., Zaidi, H.: Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning. Phys. Med. Biol. 61, 6531–6552 (2016)

    Article  Google Scholar 

  5. Burgos, N., et al.: Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning. Phys. Med. Biol. 62, 4237 (2017)

    Article  Google Scholar 

  6. Han, X.: MR-based synthetic CT generation using a deep convolutional neural network method. Med. Phys. 44, 02 (2017)

    Google Scholar 

  7. Fritscher, K., Raudaschl, P., Zaffino, P., Spadea, M.F., Sharp, G.C., Schubert, R.: Deep neural networks for fast segmentation of 3D medical images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 158–165. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_19

    Chapter  Google Scholar 

  8. Zaffino, P., et al.: Multi atlas based segmentation: should we prefer the best atlas group over the group of best atlases? Phys. Med. Biol. 63(12), 12NT01 (2018)

    Article  Google Scholar 

  9. Zaffino, P., et al.: Radiotherapy of Hodgkin and non-Hodgkin lymphoma: a nonrigid image-based registration method for automatic localization of prechemotherapy gross tumor volume. Technol. Cancer Res. Treat. 15(2), 355–364 (2016)

    Article  Google Scholar 

  10. Spadea, M.F., et al.: Contrast-enhanced proton radiography for patient set-up by using x-ray CT prior knowledge. Int. J. Radiat. Oncol. Biol. Phys. 90(3), 628–636 (2014)

    Article  Google Scholar 

  11. Zaffino, P., et al.: Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy. Phys. Med. Biol. 64(16), 165008 (2019)

    Article  Google Scholar 

  12. Bruno, P., et al.: Using CNNs for designing and implementing an automatic vascular segmentation method of biomedical images. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds.) AI*IA 2018. LNCS (LNAI), vol. 11298, pp. 60–70. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03840-3_5

    Chapter  Google Scholar 

  13. Tappeiner, E., et al.: Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach. Int. J. Comput. Assist. Radiol. Surg. 14(5), 745–754 (2019). https://doi.org/10.1007/s11548-019-01922-4

    Article  Google Scholar 

  14. Fritscher, K.D., Peroni, M., Zaffino, P., Spadea, M.F., Schubert, R., Sharp, G.: Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours. Med. Phys. 41(5), 051910 (2014)

    Article  Google Scholar 

  15. Raudaschl, P.F., et al.: Evaluation of segmentation methods on head and neck CT: auto-segmentation challenge 2015. Med. Phys. 44(5), 2020–2036 (2017)

    Article  Google Scholar 

  16. Ciardo, D., et al.: Atlas-based segmentation in breast cancer radiotherapy: evaluation of specific and generic-purpose atlases. Breast 32, 44–52 (2017)

    Article  Google Scholar 

  17. Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)

    Google Scholar 

  18. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  19. Lee, H., Chen, Y.-P.P.: Image based computer aided diagnosis system for cancer detection. Expert Syst. Appl. 42(12), 5356–5365 (2015)

    Article  Google Scholar 

  20. Moccia, S., Penza, V., Vanone, G.O., De Momi, E., Mattos, L.S.: Automatic workflow for narrow-band laryngeal video stitching. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1188–1191. IEEE (2016)

    Google Scholar 

  21. Pileggi, G., et al.: Proton range shift analysis on brain pseudo-CT generated from T1 and T2 MR. Acta Oncologica 57(11), 1521–1531 (2018)

    Article  Google Scholar 

  22. Spadea, M.F., et al.: Deep convolution neural network (DCNN) multiplane approach to synthetic CT generation from MR images-application in brain proton therapy. Int. J. Radiat. Oncol. Biol. Phys. 105(3), 495–503 (2019)

    Article  Google Scholar 

  23. Johnstone, E., et al.: Systematic review of synthetic computed tomography generation methodologies for use in magnetic resonance imaging-only radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 100(1), 199–217 (2018)

    Article  Google Scholar 

  24. Edmund, J.M., Nyholm, T.: A review of substitute CT generation for MRI-only radiation therapy. Radiat. Oncol. 12(1), 28 (2017). https://doi.org/10.1186/s13014-016-0747-y

    Article  Google Scholar 

  25. Dowling, J.A., et al.: An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 83(1), e5–e11 (2012)

    Article  Google Scholar 

  26. Xiang, L., Wang, Q., Nie, D., Qiao, Y., Shen, D.: Deep embedding convolutional neural network for synthesizing CT image from t1-weighted MR image. CoRR, vol. abs/1709.02073 (2017)

    Google Scholar 

  27. Wolterink, J.M., Dinkla, A.M., Savenije, M.H.F., Seevinck, P.R., van den Berg, C.A.T., Išgum, I.: Deep MR to CT synthesis using unpaired data. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2017. LNCS, vol. 10557, pp. 14–23. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68127-6_2

    Chapter  Google Scholar 

  28. Emami, H., Dong, M., Nejad-Davarani, S., Glide-Hurst, C.: Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Med. Phys. 45, 06 (2018)

    Article  Google Scholar 

  29. Zaffino, P., Raudaschl, P., Fritscher, K., Sharp, G.C., Spadea, M.F.: Plastimatch mabs, an open source tool for automatic image segmentation. Med. Phys. 43(9), 5155–5160 (2016)

    Article  Google Scholar 

  30. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

  31. Thummerer, A., et al.: Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy. Phys. Med. Biol. 65, 095002 (2020)

    Article  Google Scholar 

  32. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. CoRR, vol. abs/1505.04597 (2015)

    Google Scholar 

  33. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  34. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)

    Article  Google Scholar 

  35. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Calabria, Rende, Italy

    Moiz Khan Sherwani, Pierangela Bruno & Francesco Calimeri

  2. Department of Experimental and Clinical Medicine, University of Catanzaro, Catanzaro, Italy

    Paolo Zaffino & Maria Francesca Spadea

Authors
  1. Moiz Khan Sherwani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Zaffino
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierangela Bruno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria Francesca Spadea
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francesco Calimeri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Moiz Khan Sherwani .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sherwani, M.K., Zaffino, P., Bruno, P., Spadea, M.F., Calimeri, F. (2020). Evaluating the Impact of Training Loss on MR to Synthetic CT Conversion. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-64583-0_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64582-3

  • Online ISBN: 978-3-030-64583-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation