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Chapitre d'ouvrage

Evaluation of the Realism of an MRI Simulator for Stroke Lesion Prediction Using Convolutional Neural Network

Noelie Debs 1 Méghane Decroocq Tae-Hee Cho 2 David Rousseau 1 Carole Frindel 3
2 Imagerie Tomographique et Radiothérapie
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
3 Images et Modèles
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
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.
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https://hal.univ-angers.fr/hal-02512518
Contributeur : Marie-Françoise Gerard <>
Soumis le : jeudi 19 mars 2020 - 17:19:27
Dernière modification le : lundi 20 juillet 2020 - 11:54:21

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Noelie Debs, Méghane Decroocq, Tae-Hee Cho, David Rousseau, Carole Frindel. Evaluation of the Realism of an MRI Simulator for Stroke Lesion Prediction Using Convolutional Neural Network. Simulation and Synthesis in Medical Imaging, pp.151-160, 2019, ⟨10.1007/978-3-030-32778-1_16⟩. ⟨hal-02512518⟩

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