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Center for Research in Image Acquisition and Processing for Health

ERC: KOLOR SPCCT Imaging
Junior Member of the IUF

INSA Lyon, Université Claude Bernard Lyon 1, UMR CNRS 5220, Inserm U1294, Université Jean Monnet 


CREATIS is a multidisciplinary laboratory that integrates a broad range of expertise in medical imaging, playing a pivotal role in the field of health technology. It has earned recognition for its proficiency in magnetic resonance imaging, ultrasound, X-ray, and optics, supported by skills in physics, mathematics, computer science, and instrumentation. Consequently, CREATIS possesses the comprehensive capabilities required to enhance every stage of the imaging process, from acquisition to data analysis and medical interpretation. Through its enduring and collaborative partnerships with hospitals, CREATIS actively contributes to the advancement of future personalized and predictive medicine.

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Last news

22/09/2025
Cover publication in IEEE JSTQE: Hyperspectral Imaging in Neurosurgery
24/07/2025
BOOK: Clinical Applications of Spectral Photon Counting Computed Tomography Technology
24/07/2025
From sound to care: when AI decomposes the world
7/07/2025
New Gender Equality Initiatives
4/07/2025
VIP strengthens its visibility at the European level
1/07/2025
SFGBM thesis prize - 2025
11/06/2025
François Varray and Olivier Bernard appointed as Junior and Senior Members of the IUF
30/05/2025
Antoine Robert, Outstanding Presenter Award at the Fully3D 2025 conference
1/05/2025
PhD Thesis Award – University of Sherbrooke – 2025
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Presentations and seminars at CREATIS

1 Dec
Structure et explicabilité des réseaux de neurones pour la Santé: application à la prévention du sepsis
19 Sep
Spectral Speckle-Based X-Ray Phase Contrast Imaging
4 Jul
IRM quantitative pour la planification de la radiothérapie dans la zone tête et cou
3 Jul
Federated learning in neuroimage segmentation
11 Jun
Caractérisation des microstructures tissulaire et vasculaire par ultrasons
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Job opportunities

Master's subject
Stage M2, PFE ou équivalent (F/M) «Text-Guided 3D MRI Segmentation with the Segment Anything Model»
Sujet de master
Stage M2, PFE ou équivalent (F/M) «Text-Guided 3D MRI Segmentation with the Segment Anything Model»
sujet de thèse
Dynamic FES-PET/CT Imaging for Improved Breast Cancer Diagnostics and Prognostic Modeling
thesis subject
Dynamic FES-PET/CT Imaging for Improved Breast Cancer Diagnostics and Prognostic Modeling
Master's subject
Spherical deep learning models for epileptogenic zone localization
Sujet de master
Spherical deep learning models for epileptogenic zone localization
Master's subject
Spherical deep learning models for epileptogenic zone localization
Sujet de master
Spherical deep learning models for epileptogenic zone localization
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