Thomas Grenier

Category: Research

  • UNet based detection and multiple object tracking of nanoparticles

    https://www.nature.com/articles/s41598-022-06308-2 This work, carried out with IRCELYON and the laboratory Hubert Curien, presents an approach combining deep learning and computer vision for the detection and the tracking of nano-objects in situ ETEM acquisitions. It allows the extraction of complex trajectories of small objects on a noisy and non-uniform background. Its application to the analysis of…

  • Medical Deep Imaging spring school 2021

    Our third edition of the spring/summer school on deep learning for medical images will be ‘virtual’ and from 19 to 24 of April 2021. Visit the official website here. As the previous edition, there are some lectures, practices, and social events. Most of the content will be available after the school. The first edition web…

  • Medical Deep Imaging spring school 2019

    Do you want to found interesting materials to begin with deep learning for medical applications? Have a look here Materials are in Hands-on session page. Even if you don’t have a GPU card or an efficient installation of frameworks as TensorFlow or PyTorch, you can run the tutorials with your CPU. For beginners (and ones who…

  • Spatiotemporal Data Clustering

    We propose a mean-shift formulation allowing spatiotemporal clustering of video streams, and possibly extensible to other multivariate evolving data. Our formulation enables causal or omniscient filtering of spatiotemporal data, which is robust to total object occlusions. It embeds a new clustering algorithm within the filtering procedure that will group samples and reduce their number over…

  • Unifying Variational Approach and Region Growing Segmentation

    Region growing is one of the most popular image segmentation methods. The algorithm for region growing is easily understandable but criticized for its lack of theoretical background. In order to overcome this weakness, we propose to describe region growing in a new framework using a variational approach that we called Variational Region Growing (VRG). Variational…

  • USPIOs quantification in brain mice 2D MR images by default field deconvolution

    UltraSmall SuperParamagnetic Iron Oxide (USPIO) particles are used in MRI contrast agents for diagnosing different pathologies such as stroke and cancer. Determining the concentration of USPIO in MRI is of great interest. Here we present a non invasive quantification process of the USPIOs’ concentration from MR images based on the physical effect of these nanoparticles…

  • 3D Robust Adaptive Region Growing

    3D Robust Adaptive Region Growing for segmenting [18F]fluoride ion PET images

  • Multiparametric smoothing

    Multiparametric smoothing based on Mean shift procedure for ultrasound data segmentation Segmentation of ultrasound data is improved when using multi-parametric approach. In this paper we propose the use of Multi-Parametric Mean Shift procedure (MPMS). Two derived processes are described: MPMS smoothing which achieves a multi-parametric filtering in the spatial-range domain and MPMS segmentation which takes…

  • Variable bandwidth mean shift

    Variable bandwidth Mean Shift for Smoothing ultrasonic images.

  • Mean-shift filtering

    Mean shift