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Thomas GRENIER

Thomas Grenier obtained his PhD degree in Image Processing from the National Institute for Applied Sciences of Lyon (INSA Lyon) in 2005. He has been an Associate Professor at CREATIS and the Electrical Engineering department of INSA Lyon since 2006. Teacher in computer science, signal and image processing and deep learning, his research focuses on medical image segmentation with deep learning approaches.



MAIN REPONSABILITIES
  • Co-Head of the Multiple Sclerosis transversal project, on at CREATIS since 2019
  • Head of the last year course on Signal and Image processing at the Electrical Engineering department from 2016 to 2020
  • Head of the international Master IMESI from 2008 to 2014



TEACHING
  • Computer science (C/C++, DSP/GPU, microcontroler)
  • Image processing (filtering, segmentation)
  • Deep learning



COURSES
  • UNet based detection and multiple object tracking of nanoparticles
  • Machine learning medical image classification
  • Practice on medical image segmentation with UNet
  • Medical Deep Imaging spring school
  • Introduction to UNet for image segmentation
  • Introduction to image processing
  • Simple Filtering and Segmentation of medical image



PUBLICATIONS

  • Hussein BANJAK, Thomas GRENIER, Thierry EPICIER, Siddardha KONETI, Lucian ROIBAN, Anne-Sophie GAY, I.E. MAGNIN, Françoise PEYRIN et Voichita MAXIM (2018). « Evaluation of noise and blur effects with SIRT-FISTA-TV reconstruction algorithm : Application to fast environmental transmission electron tomography». In : Ultramicroscopy 189, p. 109 –123. ISSN : 03043991.
  • Sarah LECLERC, Erik SMISTAD, Joao PEDROSA, Andreas OSTVIK, Fréderic CERVENANSKY, Florian ESPINOSA, Torvald ESPELAND, Erik J. BERG, Pierre-Marc JODOIN, Thomas GRENIER, Carole LARTIZIEN, Jan DRHOOGE, Lasse LOVSTAKKEN et Olivier BERNARD (2019). « Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography ». In : IEEE Transactions on Medical Imaging 38.9, p. 2198–2210.
  • Clément DAVILLER, Thomas GRENIER, Hélène RATINEY, Michaël SDIKA, Pierre CROISILLE et Magalie VIALLON (2019). «Automatic myocardial ischemic lesion detection on magnetic resonance perfusion weighted imaging prior perfusion quantification : A pre-modeling strategy ». In : Computers in Biology and Medicine.
  • Thierry EPICIER, Hussein BANJAK, Anne-Sophie GAY, Thomas GRENIER, Sid KONETI, Voichita MAXIM et Lucian ROIBAN (2018). « Very Fast Tomography in the (E)TEM to Probe Dynamics in Materials during Operando and In Situ Experiments ». In : Microscopy and Microanalysis 24.S1, p. 1814 –1815.