Virtual PET images from CT data using deep convolutional networks: Initial results

Avi Ben-Cohen; Eyal Klang; Stephen P. Raskin; Michal Marianne Amitai; Hayit Greenspan

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

Virtual PET images from CT data using deep convolutional networks: Initial results

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

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Simulation and Synthesis in Medical Imaging - 2nd International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
Editors	Ali Gooya, Alejandro F. Frangi, Sotirios A. Tsaftaris, Jerry L. Prince
Pages	49-57
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-319-68127-6_6
State	Published - 2017
Event	2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 10 Sep 2017 → 10 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10557 LNCS

Conference

Conference	2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	10/09/17 → 10/09/17

Keywords

CT
Deep learning
Image to image
PET

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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

Cite this

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 A. Gooya, A. F. Frangi, S. A. Tsaftaris, & J. L. Prince (Eds.), Simulation and Synthesis in Medical Imaging - 2nd International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (pp. 49-57). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10557 LNCS). https://doi.org/10.1007/978-3-319-68127-6_6

Ben-Cohen, Avi ; Klang, Eyal ; Raskin, Stephen P. et al. / Virtual PET images from CT data using deep convolutional networks : Initial results. Simulation and Synthesis in Medical Imaging - 2nd International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. editor / Ali Gooya ; Alejandro F. Frangi ; Sotirios A. Tsaftaris ; Jerry L. Prince. 2017. pp. 49-57 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{416fbb9d4a9840898b0d90ab8eebfb23,

title = "Virtual PET images from CT data using deep convolutional networks: Initial results",

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.",

keywords = "CT, Deep learning, Image to image, PET",

author = "Avi Ben-Cohen and Eyal Klang and Raskin, {Stephen P.} and Amitai, {Michal Marianne} and Hayit Greenspan",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 ; Conference date: 10-09-2017 Through 10-09-2017",

year = "2017",

doi = "https://doi.org/10.1007/978-3-319-68127-6_6",

language = "الإنجليزيّة",

isbn = "9783319681269",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "49--57",

editor = "Ali Gooya and Frangi, {Alejandro F.} and Tsaftaris, {Sotirios A.} and Prince, {Jerry L.}",

booktitle = "Simulation and Synthesis in Medical Imaging - 2nd International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Proceedings",

}

Ben-Cohen, A, Klang, E, Raskin, SP, Amitai, MM & Greenspan, H 2017, Virtual PET images from CT data using deep convolutional networks: Initial results. in A Gooya, AF Frangi, SA Tsaftaris & JL Prince (eds), Simulation and Synthesis in Medical Imaging - 2nd International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10557 LNCS, pp. 49-57, 2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 10/09/17. https://doi.org/10.1007/978-3-319-68127-6_6

Virtual PET images from CT data using deep convolutional networks: Initial results. / Ben-Cohen, Avi; Klang, Eyal; Raskin, Stephen P. et al.
Simulation and Synthesis in Medical Imaging - 2nd International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. ed. / Ali Gooya; Alejandro F. Frangi; Sotirios A. Tsaftaris; Jerry L. Prince. 2017. p. 49-57 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10557 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Virtual PET images from CT data using deep convolutional networks

T2 - 2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017

AU - Ben-Cohen, Avi

AU - Klang, Eyal

AU - Raskin, Stephen P.

AU - Amitai, Michal Marianne

AU - Greenspan, Hayit

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - CT

KW - Deep learning

KW - Image to image

KW - PET

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

M3 - منشور من مؤتمر

SN - 9783319681269

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 49

EP - 57

BT - Simulation and Synthesis in Medical Imaging - 2nd International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Proceedings

A2 - Gooya, Ali

A2 - Frangi, Alejandro F.

A2 - Tsaftaris, Sotirios A.

A2 - Prince, Jerry L.

Y2 - 10 September 2017 through 10 September 2017

ER -

Ben-Cohen A, Klang E, Raskin SP, Amitai MM, Greenspan H. Virtual PET images from CT data using deep convolutional networks: Initial results. In Gooya A, Frangi AF, Tsaftaris SA, Prince JL, editors, Simulation and Synthesis in Medical Imaging - 2nd International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. 2017. p. 49-57. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-319-68127-6_6

Virtual PET images from CT data using deep convolutional networks: Initial results

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

UN SDGs

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