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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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
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
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)
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)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint (2016). arXiv:1611.07004
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)
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)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
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
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint (2014). arXiv:1409.1556
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)
Weber, W.A.: Assessing tumor response to therapy. J. Nucl. Med. 50(Suppl 1), 1S–10S (2009)
Acknowledgment
This research was supported by the Israel Science Foundation (grant No. 1918/16).
Part of this work was funded by the INTEL Collaborative Research Institute for Computational Intelligence (ICRI-CI).
Avi Ben-Cohen’s scholarship was funded by the Buchmann Scholarships Fund.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ben-Cohen, A., Klang, E., Raskin, S.P., Amitai, M.M., Greenspan, H. (2017). Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results. In: Tsaftaris, S., Gooya, A., Frangi, A., Prince, J. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2017. Lecture Notes in Computer Science(), vol 10557. Springer, Cham. https://doi.org/10.1007/978-3-319-68127-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-68127-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68126-9
Online ISBN: 978-3-319-68127-6
eBook Packages: Computer ScienceComputer Science (R0)