Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results

Authors : Avi Ben-Cohen, Eyal Klang, Stephen P. Raskin, Michal Marianne Amitai, Hayit Greenspan

Published in: Simulation and Synthesis in Medical Imaging

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this work we present a novel system for PET estimation using CT scans. We explore the use of fully convolutional networks (FCN) and conditional generative adversarial networks (GAN) to export PET data from CT data. Our dataset includes 25 pairs of PET and CT scans where 17 were used for training and 8 for testing. The system was tested for detection of malignant tumors in the liver region. Initial results look promising showing high detection performance with a TPR of 92.3% and FPR of 0.25 per case. Future work entails expansion of the current system to the entire body using a much larger dataset. Such a system can be used for tumor detection and drug treatment evaluation in a CT-only environment instead of the expansive and radioactive PET-CT scan.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Region-Enhanced Joint Dictionary Learning for Cross-Modality Synthesis in Diffusion Tensor Imaging
next chapter Semi-supervised Assessment of Incomplete LV Coverage in Cardiac MRI Using Generative Adversarial Nets
Literature
1.
Adelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M.: Pyramid methods in image processing. RCA Eng. 29(6), 33–41 (1984)
2.
Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., Greenspan, H.: Fully convolutional network for liver segmentation and lesions detection. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 77–85. Springer, Cham (2016). doi:10.​1007/​978-3-319-46976-8_​9
3.
Christ, P.F., Ettlinger, F., Grün, F., Elshaera, M.E.A., Lipkova, J., Schlecht, S., Rempfler, M.: Automatic liver and tumor segmentation of CT and MRI Volumes using cascaded fully convolutional neural networks. arXiv preprint (2017). arXiv:​1702.​05970
4.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
5.
Higashi, K., Clavo, A.C., Wahl, R.L.: Does FDG uptake measure the proliferative activity of human cancer cells? In vitro comparison with DNA flow cytometry and tritiated thymidine uptake. J. Nuclear Med. 34, 414 (1993)
6.
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint (2016). arXiv:​1611.​07004
7.
Kelloff, G.J., Hoffman, J.M., Johnson, B., Scher, H.I., Siegel, B.A., Cheng, E.Y., Shankar, L.: Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development. Clin. Cancer Res. 11(8), 2785–2808 (2005)CrossRef
8.
Kinehan, P.E., Fletcher, J.W.: PET/CT standardized uptake values (SUVs) in clinical practice and assessing response to therapy. Semin. Ultrasound CT MRI 31(6), 496–505 (2010)CrossRef
9.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
10.
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi:10.​1007/​978-3-319-24574-4_​28 CrossRef
11.
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2016)CrossRef
12.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint (2014). arXiv:​1409.​1556
13.
Weber, W.A., Grosu, A.L., Czernin, J.: Technology insight: advances in molecular imaging and an appraisal of PET/CT scanning. Nature Clin. Pract. Oncol. 5(3), 160–170 (2008)CrossRef
14.
Weber, W.A.: Assessing tumor response to therapy. J. Nucl. Med. 50(Suppl 1), 1S–10S (2009)CrossRef
Metadata
Title
Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results
Authors
Avi Ben-Cohen
Eyal Klang
Stephen P. Raskin
Michal Marianne Amitai
Hayit Greenspan
Copyright Year
2017
Book
Simulation and Synthesis in Medical Imaging

Print ISBN: 978-3-319-68126-9

Electronic ISBN: 978-3-319-68127-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68127-6_6

Premium Partner

    Image Credits