Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network

Noëlie Debs; Méghane Decroocq; Tae Hee Cho; David Rousseau; Carole Frindel

doi:10.1007/978-3-030-32778-1_16

Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network

Noëlie Debs, Méghane Decroocq, Tae Hee Cho, David Rousseau, Carole Frindel

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

Abstract

We are focusing on the difficult task of predicting final lesion in stroke, a complex disease that leads to divergent imaging patterns related to the occluded artery level and the geometry of the patient’s vascular tree. We propose a framework in which convolutional neural networks are trained only from synthetic perfusion MRI - obtained from an existing physical simulator - and tested on real patients. We incorporate new levels of realism into this simulator, allowing to simulate the vascular tree of a given patient. We demonstrate that our approach is able to predict the final infarct of the tested patients only from simulated data. Among the various simulated databases generated, we show that simulations taking into account the vascular tree information give the best classification performances on the tested patients.

Original language	English
Title of host publication	Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
Editors	Ninon Burgos, Ali Gooya, David Svoboda
Publisher	Springer
Pages	151-160
Number of pages	10
ISBN (Print)	9783030327774
DOIs	https://doi.org/10.1007/978-3-030-32778-1_16
Publication status	Published - 2019
Externally published	Yes
Event	4th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 2019 Oct 13 → 2019 Oct 13

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11827 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	19/10/13 → 19/10/13

Keywords

Arterial input function
Convolutional neural network
Lesion prediction
Perfusion MRI
Simulation
Time of transport

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-32778-1_16

Cite this

Debs, N., Decroocq, M., Cho, T. H., Rousseau, D., & Frindel, C. (2019). Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network. In N. Burgos, A. Gooya, & D. Svoboda (Eds.), Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 151-160). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11827 LNCS). Springer. https://doi.org/10.1007/978-3-030-32778-1_16

Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network. / Debs, Noëlie; Decroocq, Méghane; Cho, Tae Hee et al.
Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Proceedings. ed. / Ninon Burgos; Ali Gooya; David Svoboda. Springer, 2019. p. 151-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11827 LNCS).

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

Debs, N, Decroocq, M, Cho, TH, Rousseau, D & Frindel, C 2019, Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network. in N Burgos, A Gooya & D Svoboda (eds), Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11827 LNCS, Springer, pp. 151-160, 4th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019, Shenzhen, China, 19/10/13. https://doi.org/10.1007/978-3-030-32778-1_16

Debs N, Decroocq M, Cho TH, Rousseau D, Frindel C. Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network. In Burgos N, Gooya A, Svoboda D, editors, Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Proceedings. Springer. 2019. p. 151-160. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32778-1_16

Debs, Noëlie ; Decroocq, Méghane ; Cho, Tae Hee et al. / Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network. Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Proceedings. editor / Ninon Burgos ; Ali Gooya ; David Svoboda. Springer, 2019. pp. 151-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{6c39bd9343874b0fb822d4a875925cb5,

title = "Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network",

abstract = "We are focusing on the difficult task of predicting final lesion in stroke, a complex disease that leads to divergent imaging patterns related to the occluded artery level and the geometry of the patient{\textquoteright}s vascular tree. We propose a framework in which convolutional neural networks are trained only from synthetic perfusion MRI - obtained from an existing physical simulator - and tested on real patients. We incorporate new levels of realism into this simulator, allowing to simulate the vascular tree of a given patient. We demonstrate that our approach is able to predict the final infarct of the tested patients only from simulated data. Among the various simulated databases generated, we show that simulations taking into account the vascular tree information give the best classification performances on the tested patients.",

keywords = "Arterial input function, Convolutional neural network, Lesion prediction, Perfusion MRI, Simulation, Time of transport",

author = "No{\"e}lie Debs and M{\'e}ghane Decroocq and Cho, {Tae Hee} and David Rousseau and Carole Frindel",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 4th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 13-10-2019",

year = "2019",

doi = "10.1007/978-3-030-32778-1_16",

language = "English",

isbn = "9783030327774",

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

publisher = "Springer",

pages = "151--160",

editor = "Ninon Burgos and Ali Gooya and David Svoboda",

booktitle = "Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Proceedings",

}

TY - GEN

T1 - Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network

AU - Debs, Noëlie

AU - Decroocq, Méghane

AU - Cho, Tae Hee

AU - Rousseau, David

AU - Frindel, Carole

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - We are focusing on the difficult task of predicting final lesion in stroke, a complex disease that leads to divergent imaging patterns related to the occluded artery level and the geometry of the patient’s vascular tree. We propose a framework in which convolutional neural networks are trained only from synthetic perfusion MRI - obtained from an existing physical simulator - and tested on real patients. We incorporate new levels of realism into this simulator, allowing to simulate the vascular tree of a given patient. We demonstrate that our approach is able to predict the final infarct of the tested patients only from simulated data. Among the various simulated databases generated, we show that simulations taking into account the vascular tree information give the best classification performances on the tested patients.

AB - We are focusing on the difficult task of predicting final lesion in stroke, a complex disease that leads to divergent imaging patterns related to the occluded artery level and the geometry of the patient’s vascular tree. We propose a framework in which convolutional neural networks are trained only from synthetic perfusion MRI - obtained from an existing physical simulator - and tested on real patients. We incorporate new levels of realism into this simulator, allowing to simulate the vascular tree of a given patient. We demonstrate that our approach is able to predict the final infarct of the tested patients only from simulated data. Among the various simulated databases generated, we show that simulations taking into account the vascular tree information give the best classification performances on the tested patients.

KW - Arterial input function

KW - Convolutional neural network

KW - Lesion prediction

KW - Perfusion MRI

KW - Simulation

KW - Time of transport

UR - http://www.scopus.com/inward/record.url?scp=85075643085&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85075643085&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-32778-1_16

DO - 10.1007/978-3-030-32778-1_16

M3 - Conference contribution

AN - SCOPUS:85075643085

SN - 9783030327774

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

SP - 151

EP - 160

BT - Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Proceedings

A2 - Burgos, Ninon

A2 - Gooya, Ali

A2 - Svoboda, David

PB - Springer

T2 - 4th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019

Y2 - 13 October 2019 through 13 October 2019

ER -

Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this