Chantal MULLER
Présentation
Chantal Revol-Muller is Associate Professor (Maître de Conférences Hors Classe) at INSA Lyon's Telecommunications Department and researcher at CREATIS laboratory (CNRS UMR 5220, INSERM U1294) in the MYRIAD team.
Her research focuses on medical image analysis using AI, with expertise in segmentation algorithms, adaptive filtering, and generative AI for medical imaging.
Since 2023, she has been developing approaches combining deep learning and diffusion models for brain MRI analysis, with applications in multiple sclerosis lesion segmentation and text-conditioned synthetic image generation. She currently directs a PhD thesis on multimodal text-image latent representation fusion in generative AI for medical imaging.
Her recent work includes international collaborations (UNC Chapel Hill USA, ENET'Com Tunisia) and interdisciplinary projects in biology and catalysis.
She previously directed the apprenticeship program at INSA Lyon's Telecommunications Department (2017-2023).
Domaines de recherche
Compétences
Publications
Publications
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Détection et segmentation automatisées de bactériocytes à partir d'images de microscopie optiqueGroupe de Recherche et d'Etudes de Traitement du Signal et des Images (GRETSI), Aug 2025, Strasboug, France |
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Comparative analysis of three advanced deep learning algorithms for Multiple Sclerosis lesion segmentation in FLAIR MRIIEEE ICSP’24 & 5th Sino-French Workshop 2024 “Medical Image Analysis and AI (MAI)”, IEEE Beijing Section, Beijing Jiaotong University, and Soochow University, Oct 2024, Suzhou, China, France. pp.697-702, ⟨10.1109/ICSP62129.2024.10846353⟩ |
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DALL-E Brain: Generating 2D T1-w, T2-w, and FLAIR Brain MRI Images from Textual PromptsEUSIPCO 2025, European Association for Signal Processing (EURASIP), Sep 2025, Palermo, France |
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DALL-E Brain : Génération d'images IRM cérébrales 2D T1-w, T2-w et FLAIR à partir de descriptions textuellesColloque GRETSI'25, XXXe Colloque Francophone de Traitement du Signal et des Images, Aug 2025, Strabourg, France |
Application of Neural Networks to the Segmentation of Nanocrystals and Liquid Nanodrops during in situ Condensation of Water in the Environmental Transmission electron microscope.IMC20 The 20th International Microscopy Congress, Sep 2023, Busan (Corée), South Korea |
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Modeling 3D realistic organic tissues from 2D digital microscopy images6th annual Winter Conference of the European Society for Molecular Imaging : hot TOPics in molecular IMaging (TOPIM), Apr 2012, Les Houches, France |
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Segmentación del árbol vascular pulmonarConferencia Latinoamericana en Informática (CLEI), Oct 2012, Medellin, Colombia |
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Region Growing: Adolescence and adulthood ;Two visions of region growing: in feature space and variational frameworkVISAPP 2012. International Conference on Computer Vision Theory and Applications, 2012, Rome, Italy. pp.286-297 |
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Feature space region growingIEEE International Conference On Image Processing, Sep 2012, Orlando, United States. pp.2585 - 2588, ⟨10.1109/ICIP.2012.6467427⟩ |
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Age estimation from 3D X-ray CT images of human fourth ribsWorldcomp'12: 16th International Conference on Image Processing, Computer Vision, & Pattern Recognition IPCV'12, Jul 2012, Las Vegas, United States. pp.Unknown |
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3D nano-CT: a new approach for imaging at cellular scaleGeneral Assembly of the French Society for Signal and Image Processing in Life Sciences, Oct 2012, Lyon, France |
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Shape Prior in Variational Region GrowingInternational Conference on Image Processing Theory, Tools and Applications, IPTA'12, Oct 2012, Istanbul, Turkey. pp.116 - 120, ⟨10.1109/IPTA.2012.6469571⟩ |
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CreaLungs: a variational region growing method to segment pulmonary vascular treesISBI 2012 VESSEL12 Challenge, May 2012, Barcelona, Spain |
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Imaging of the Bone Cell Network with Nanoscale Synchrotron Radiation Computed TomographySSBA 2012, Symposium on Image Analysis, Mar 2012, Stockholm, Sweden |
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Segmentation of 3D cellular networks from SR-micro-CT images2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Mar 2011, Chicago, United States. pp.1970-1973, ⟨10.1109/ISBI.2011.5872796⟩ |
Unifying variational approach and region growing segmentation18th European Signal Processing Confernce (EUSIPCO-2010), 2010, Aalborg, Denmark. pp.1781-1785 |
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Variational Region GrowingVISAPP 2009 - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, Lisboa, Portugal, February 5-8, 2009 - Volume 2, Feb 2009, Lisbonne, Portugal. pp.166-171 |
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3D Robust Adaptive Region Growing for Segmenting [18F]fluoride Ion PET Images2006 NSS MIC, 2006, San Diego, USA, Unknown Region. pp.2644-2648 |
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Hybrid approach for multiparametric mean shift filteringICIP\textquoteright06, 2006, Atlanta, USA, Unknown Region. pp.1541-1544 |
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Lissage et segmentation d\textquoterightimages multi-paramétriques ultrasonores par une approche \textquoterightMean shift\textquoterightGRETSI\textquoteright05, 2005, Louvain-La-Neuve, Belgium. pp.21-24 |
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textquoterightMean Shift\textquoteright Adaptatif pour le lissage d\textquoterightimages ultrasonoresGRETSI\textquoteright05, 2005, Louvain-La-Neuve, Belgium. pp.53-56 |
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Variable bandwidth Mean Shift for Smoothing ultrasonic imagesEUSIPCO\textquoteright05, 2005, Antalya, Turkey. Article ID cr1454, 4 p |
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Multiparametric Smoothing Based On Mean Shift Procedure For Ultrasound Data SegmentationEUSIPCO\textquoteright05, 2005, Antalya, Turkey. Article ID cr1461, 4 p |
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Automated seeds location for whole body NaF PET segmentationIEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference, 2003, Portland, OR, USA, Unknown Region. pp.2210-2214 |
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Automated detection and segmentation of bacteriocytes from light microscopy imagesColloque d'Intelligence Artificielle en Imagerie Biomédical (IABM), Mar 2025, Nice, France |
Region Growing: When Simplicity Meets Theory - Region Growing Revisited in Feature Space and Variational FrameworkG. Csurka, M. Kraus, R. S. Laramee, P. Richard, J. Braz. Communications in Computer and Information Science Computer Vision, Imaging and Computer Graphics. Theory and Application, Springer, pp.426-444, 2013, 978-3-642-38241-3. ⟨10.1007/978-3-642-38241-3_29⟩ |
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Prise en compte de données multidimensionnelles et hétérogènes en imagerie médicale : contributions en fusion, filtrage et segmentationSciences de l'ingénieur [physics]. Université Claude Bernard Lyon1; INSA Lyon, 2012 |