Head of Software Development at Biomedical Imaging Group BIG
School of Engineering STI
EPFL Center of Imaging ECI
Projects & Code
Collaboration with the team of Matthias Lutolf, EPFL
Collaboration with Juliette Griffié, SB, EPFL
Material Science and Environmental Science
|Image Processing Programmation in Java (ImageJ)||Jupyter Notebooks for Image Processing Programming|
|7 graded sessions/year from 2000 to 2020||7 graded sessions/year from 2020|
These computer sessions are engaging students to practice IP by developing some basic
algorithms in Java.
This series of notebooks are designed to teach image-processing programming
|D. Sage and M. Unser, A pedagogical tool for teaching IP programming in Java, IEEE ICIP 2001.||Video tutorial of Pol del Aguila Pla, Remote practical labs with personalized feedback|
Lectures for Ph.D Student
University of Gothenburg Sweden, Conf. Universitaire Suisse Occidentale, EU FP-7 Pardem
Zero-Code Deep-Learning Solutions for Bioimage Analysis
MIFOBIO, CNRS RT-MFM, ANF-Deepscopie, ZIDAS
- Dr. Laurène Donati, Reconstruction Methods for Cryo-Electron Microscopy: From Model-based to Data-driven, EPFL EDEE 2020.
- Dr. Anaïs Badoual, Subdivide and Conquer: Active Contours and Surfaces for Biomedical Image Segmentation, EPFL EDEE 2019.
- Dr. Daniel Schmitter, Spline Shape Processing: Representation, Learning, and Modeling, EPFL EDEE 2017.
- Guillaume Maucort, Machine learning pour l’imagerie microscopique quantitative sans marquage. Applications à l’imagerie biomédicale. Univerté de Bordeaux, 2022.
- Dr. Olivier Lévêque, Co-design of imaging systems for depth-of-field extension - Application to single-molecule localization, Univerté Paris-Saclay, 2022.
- Dr. Estibaliz Gómez-de-Marisca (Report), Insights to the characterization of cell motility and intercellular communication through a bioimage analysis perspective, Universidad Carlos III de Madrid, 2021.
- Dr. Vasileios Angelopoulos, Development and Evaluation of Galaxy Shape Measurement Algorithms for Radio Interferometric Data, EPFL EDEE 2021.
- Dr. Christian V. Hansen, Computational Modeling of Fluorescence Photobleaching, Mathematics, University of Southern Denmark, 2018.
- Dr. Hugo Rositi, Imagerie de contraste de phase par rayonnement synchrotron, Biomedical Engineering, INSA Lyon, 2017.
- Alejandro Noguerón Arámburu, Accurate Pattern Estimation in Structured Illumination Microscopy, Master Thesis, 2022.
- Alexandru-Petru Vasile, Directional image analysis in 3D - Python implemenation, Imaging intership, 2022.
- Busra Bulut, Optimal methods for cell lineage reconstruction, Imaging intership, 2022.
- Lucia Moya Sans, Advanced development of deepImageJ - Classic and deep learning denoising methods, EPFL Excellence in Engineering, 2022.
- Quentin Juppet, Image Analysis Pipeline for Multiplex Immunofluorescence with Machine Learning, Master at Lunaphore S.A., 2021.
- Eliana Renzo, Clustering Analysis of SMLM, EPFL, Biophysics, 2021.
- Mehrsa Pourya, Hierarchical Cluster Analysis of SMLM Data, Sharif University of Technology, Tehran, Iran, 2020.
- Carlos García-López-de-Haro, Deep Image Prior for Extended Depth of Field Microscopy, Master Degree UC3M, Madrid, Spain, 2020.
- Quentin Juppet, Deep Learning Enables Individual Cell Classification in Histological Images, Internship EPFL
- Estibaliz Gómez-de-Marisca, DeepImageJ: User-friendly tool to run deep learming model in ImageJ, Mobility Program of Neubias, 2020.
- Robin Lang, SMLM web viewer, Master IC-EPFL, 2018.
- Dr. Silvia Collabrese, Machine Learning Techniques on 2D and 3D SMLM, Ph.D. Internship, IIT Genova, Italy, 2017
- Emmanuel Froustey, Internship, 2017. Phase retrieval by using transport-of-intensity equation,
- Marta Alabrudzinska, Segmentation of intravital images for quantiﬁcation of tumor growth, Ph.D. Internship, KI, Sweden 2016.
- Pelin Dogan, Tribology and image processing, EMPA, Thun, Switzerland, 2015.
- Raghavender Sahdev, Drift correction of in time-lapse microscopy, Google Summer of School, 2015.
- Dr. Olivia Mariani, Interactive tool for Image sequence analysis, Internship EPFL SV, 2014.
- Dr. Daniel Schmitter, Tracking fluorescently labeled structures in rod-shaped cells, Synergia SFNS Project, 2013.
- Roland Nüssbaum, Image calibration for super-resolved SIM, Master STI-EPFL, 2013
- Laurent Nguyen, Vision-based system for the control of wastewater, Master ENAC-EPFL, 2011.
- Dr. Zsusanna Püspöki, Fast space-variant Image filtering, Internship, Budapest, 2010.
- Dr. Stefan Geissbühler, Evolutionary snake algorithms for biological applications, Master STI-EPFL, 2008.
- Dr. Aurélien Stalder, Fast non-axisymmetric drop shape analysis, Master STI-EPFL, 2007.
- Dr. Michel Tsukahara, Coupled tomography to exploring the granular media microstructure, Master SB-EPFL, 2006.
- Charles Berger, Automatic Bone segmentation with active parametric contour in CT images, Collaboration with Pablo Garcia-Amorena, Mirrakoi SA, 2022.
- Eugénie Demeure, Image-based quantification of cell blebbing, Collaboration with Prof. Sandra Sousa, University of Porto, 2021.
- Kay Lächer, Deep neural network for SIM super-resolution reconstruction with a reduced number of images, Collaboration with Emmanuel Soubies, IRIT, Toulouse, 2021.
- Héloïse Monnet, Growth of E. coli cells: simulation of images data to training of a neural network, Collaboration with Prof. McKinney, EPFL, 2020
- Alexandre Levy, Review and implementation of loss functions for image-to-image neural network, Collaboration with Prof. Muñoz Barrutia, UC3M, Madrid, 2020.
- Emma Bouton-Bessac, Dynamic of cracks in wall based on analysis of image sequence, Collaboration with Amir Rezaie, ENAC, 2020.
- Quentin Juppet, Deep Learning and Image Analysis to Distinguish Murine form Human cells, Collaboration with Prof. Brisken, EPFL, 2020.
- Rémy Dornier, Image analysis algorithm for crack detection in walls, Collaboration with Prof. Katrin Beyer and Dr. Amir Rezaie, 2020.
- Shad Ali Durussel, Image analysis to monitor the growth of lithium particles in TEM images, Collaboration with Prof. Tileli Vasiliki, EPFL, 2019
- Robin Lang, Simulating realistic synthetic data sets for developing a self-driving microscope, Collaboration with Dr. Juliette Griffié, EPFL, 2019
- Mai Yuanfei, Quantifying cell cycle-gated expression by machine Learning, Collaboration with Prof. David Suter, EPFL, 2019
- Robin Lang, Web tools for image processing for SMLM, 2019.
- Arthur Benzaquin, Monitoring plant phenotyping by time-lapse video, Collaboration with Prof. Colin Jones, EPFL, 2018.
- Cyril Favre, Didactic demonstrations for image-processing courses, 2017.
- Aymeric Galan, Active Contour for Jointly Segmentation of Multiple Cells, Collaboration with Dr. A. Badoual, 2018.
- Florian Poma, Quantification of fiber-like structures in time-lapse fluorescence microscopy images, with Prof. Pierre Gönczy, EPFL, 2017
- Jean Frédéric Haizmann, Analysis of cortical structures from 3D live imaging of C. elegans embryos, with Prof. Pierre Gönczy EPFL, 2017
- Baptiste Sottas, Analysis of cortical structures from live imaging of C. elegans embryos, 2016.
- Brune Bastide and Axel Vandebrouck, Segmentation of Doppler ultrasound images for monitoring the blood flow, Collaboration with Dr. Raoul Schorer (HUG), 2016.
- Ariane Kaeppeli, Quantification of the host pathogen interactions, with Dr. Matthieu Delincé, 2015.
- Luc Girod , Morphometric measurement of larvae of fish by image analysis, with Uni. of Lausanne, 2015.
- Roland Nussbaumer, Image Calibration for Structured Illumination Microscopy, 2015.
- Amicie De Pierrefeu, Tracking flagella undulations in microscopy images, 2014.
- Charlotte Juillard, Deconvolution in biomicroscopy, 2014.
- Benjamin D'incau, Swallowing Human, with Nestlé Research Center, 2013
- Lukas De Oliveira Prestes, Applet in Java for the Representation of Shapes Using Fourier Series, 2013.
- Chen Zhiwei, Characterization of G protein coupled receptors, with Dr. S. Roizard, SB, 2012
- Philippe Hanhart, Monitoring the PSF of a microscope for 3D life cell imaging, 2012.
- Cléo Moulin, Processing biological images using the fast bilateral filter, 2011.
- Patrizia Spoerri, Estimation of the 3D structure in PALM, with Dr. H. Kirshner, 2011.
- Bergem Yannick, Analyse du comportement de déversoirs d’orage with Dr. L. Rossi, ENAC, 2010.
- François Curdy, Collagen filaments detection in 3D, with Dr. Alessandra Griffa, 2010.
- Ulugbek Kamilov, Image Denoising Using Several Acquisitions, with Dr. F. Luisier, 2009.
- Virginie Uhlmann, Implementation and comparison of keypoint detectors, 2009.
- Anil Yuce , Bilateral spline filters for image segmentation, with Dr. C. S. Seelamantula, 2009.
- Stefan Geissbuehler, Evolutionary snake algorithms for biological applications, 2008.
- Claire Verburgh, Estimation du déplacement des vagues, with Prof. Marcel Salathé, 2008.
- Francis Géroudet, Navigation à travers des très grandes images de microscopie, 2008.
- Joël Leuenberger, Color image Segmentation in Optical Microscopy, 2007.
- Céline Di Venuto, Quantify vessels growing in the chloro-allantoic membrane, 2007.
- Xavier Winterhalter, Analyse de traces de particules mobiles dans des images, 2007.
- Nicole Brueschweiler, Reconstruction 3D des réseaux vascualaires en imagerie médicale
- Florent Cosandier, Quantitative measurement of chromatin condensation in 4D
- Thomas Lemmin, Démonstrateur Web pour la compréhension des images numériques
- Christophe Magnard, Automatic calibration for panoramic camera
- Nicolas Pavillon, Détection de contour avec une précision sub-pixel
- Joy Anushini Ariarajah, Applet de démonstration pour le filtrage d'images chez Fourier
- Loïc Segapelli, Suivi de vibrisses de souris pour l'étude du comportement sensoriel
- Joël Arnold, Neuron tracing in 3D
- Anna Larsson, Identification de glomérules par analyse d'image du système olfactif
- Sadasing Kowlessur, Trajectométrie de souris pour des études de comportement en neuroscience
- Christophe Magnard, Microscope virtuel à but didactique pour Internet
- Aurélien Stalder, Analyse d'images pour la caractérisation de traitement de surface
- Raphaël Tornay, Assemblage et homogénéisation d'images pour les neurosciences
- Fabien Saint-Roch, Analyse d'image pour l'étude de la dynamique de cellules souches
- Annick Marin, Localisation de chromosomes à travers une série d'images
- Florian Marty, Relevé de l'arbre dendritique dans des images confocales 3D
- Vahid Fahfouri, Reconnaissance automatique de la texture pulmonaire
- Jesse Berent, Synthèse d'images et détermination de la topologie par fusion de série focale
Tracking of neurons in sequence of images
- Ambroise Krebs, Analyse d’image pour la détection de syncytia en thérapie anti-VIH
- Daniel Stadelmann, Détection de chocolats par Watershed
- Roland Michaely, Detection of vascular diameters by ultrasonic imaging
- Sacha Haymoz, Image Processing applied on micro-arrays for molecular biology
- David Leroux, Zoom in Java
- Delphine Perrottet, Image Processing on Microarrays
- Bernhard Petersch, Detection of contours in biomedical images using dynamic programming
- E. Gómez-de-Mariscal, ... D. Sage, DeepImageJ: A user-friendly environment to run deep learning models in ImageJ, Nature Methods, 18, 2021.
- V. Uhlmann, L. Donati, D. Sage, A Practical Guide to Supervised Deep Learning for Bioimage Analysis, IEEE Signal Processing Magazine, 39, 2022.
- Q. Juppet, ... D. Sage, Deep Learning Enables Individual Xenograft Cell Classification by Analysis of Contextual Features, Journal of Mammary Gland Biol Neoplasia, 2021.
- D. Sage et al., Super-Resolution Fight Club: Assessment of 2D and 3D Single-Molecule Localization Microscopy Software, Nature Methods, 16, 2019.
- D. Sage et al., DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy, Methods—Image Processing for Biologists, 115, February 15, 2017.
- D. Sage et al., Quantitative Evaluation of Software Packages for Single-Molecule Localization Microscopy, Nature Methods, 12, 2015.
- D. Sage et al., A Software Solution for Recording Circadian Oscillator Features in Time-Lapse Live Cell Microscopy, Cell Division, 5, 2010.
- D. Sage et al., Automatic Tracking of Individual Fluorescence Particles: Study of Chromosome Dynamics, IEEE Transactions on Image Processing, 14, 2005.
Microscopy Image Analysis: The Shift to Deep Learning?
CNRS School, Functional Microscopy for Biology (MiFoBio'21), Giens, France, November 5-12, 2021.
The quantification of microscopy images requires automatic tools to extract relevant information from complex data. To tackle this task, numerous image analysis algorithms have been designed, commonly based on prior knowledge and on physical modeling. However, the recent success of the deep learning (DL) in computer science have drastically changed the bioimage analysis workflows to a data-centric paradigm. While this DL technology remains relatively inaccessible to end-users, recent efforts has been proposed to facilitate the deployment of DL for some bioimage applications through new open-source software packages. Here, we present a set of user-friendly tools that allows to test DL models and to gain proficiency in DL technology: the centralized repository of bioimage model (Bioimage Model Zoo), the ready-to-use notebooks for the training, and the plugin deepImageJ that can run a DL model in ImageJ. We provide also good practice tips to avoid the risk of misuses. We address some practical issues such as the availability of massive amount of images, the understanding of generalizability concept, or the selection of the pre-trained models. The shift to deep learning also questions the community about the trust, the reliability and the validity of such trained deep learning models.
Colloque de la Société Française de Microscopie, Reims, France, Juillet 5-9, 2021.
The quantification of microscopy images require automatic tools to extract relevant information from complex data. To tackled this task, numerous image analysis algorithms have been designed, commonly based on prior knowledge and on physical modeling. However, the recent success of the deep learning (DL) in computer science have drastically changed the bioimage analysis workflows to a data-centric paradigm. While this DL technology remains relatively inaccessible to end-users, recent efforts has been proposed to facilitate the deployment of DL for some bioimage applications through new open-source software packages. Here, we present a set of user-friendly tools that allows to test DL models and to gain proficiency in DL technology: the centralized repository of bioimage model (Bioimage Model Zoo), the readytouse notebooks for the training, and the plugin deepImageJ that can run a DL model in ImageJ. We provide also good practice tips to avoid the risk of misuses. We address some practical issues such as the availability of massive amount of images, the understanding of generalizability concept, or the selection of the pre-trained models. The shift to deep learning also questions the community about the trust, the reliability and the validity of such trained deep learning models.
Achieving Higher Resolution in 3D Fluorescence Imaging: Deconvolution Microscopy and Single-Molecule Localization Microscopy
Journées imagerie optique non conventionnelle (JIONC), Paris, France, March 11, 2020.
Advanced microscopy techniques yield outstanding images (3D, time-lapse, multichannel), allowing one to address fundamental questions in developmental biology, molecular biology and neuroscience. Most of these techniques deploy computational methods that numerically reconstruct high-resolution or super-resolution images from the degraded measurements. A faithful reconstruction of a 3D image requires knowledge of the image acquisition model which mainly consists of the 3D point-spread function (PSF). In this presentation, I shall review two such techniques that highly rely on the PSF: 1) 3D deconvolution microscopy that helps to remove the out-of-focus and to improve the contrast of 3D images, and 2) 3D single-molecule localization microscopy that allows one to achieve super-resolution images (∼25 nm in the lateral plane, ∼75 nm in the axial direction). This presentation is based on our experience of organizing a grand challenge to benchmark a wide range of softwares on the same reference datasets.
Computational Bioimaging—Selected Topics
Journée MICA: Analyse d'images et bases de données, Nice, France, January 22, 2019.
Recent advances in microscope technology combined with powerful computers now provide outstanding images (3D, time-lapse, multichannel, fluorescence), allowing one to address fundamental questions in developmental biology, molecular biology and neuroscience. The analysis of this unprecedented flow of imaging data requires the development of specific software tools to numerically reconstruct images and to automatically perform segmentation, quantification and tracking of structures of interest. This has led to the emergence of a new field of research, #bioimage informatics# which aims to develop computational procedures to process, analyze, and visualize light microscopy images. Here, we report our experience in the development of open-source software tools. These tools are written as Java plugins for the popular open-source software suites: ImageJ, Fiji or Icy. We address the following topics with are common problems in computational bioimaging: the 3D microscopy deconvolution, the directional image analysis for detecting filament-like structure, the deformable model (snake) for segmentation, and the tracking of bright spots in noisy images.
Performing 3D Super-Resolution Reconstruction Using Open-Source Software
Quantitative Bioimaging Conference (QBI), Rennes, France, January 9, 2019.
The 3D SMLM reconstruction is a challenging computational task in term of performances, parametrization and runtime. The goal of this workshop is to present and to experiment some of the existing software solutions. We selected some software based on their usability and their accessibility (open-source) on ImageJ or Matlab. We will experiment software in four different modalities: 2D, astigmatism, double-helix, and biplane, both on simulated datasets and real datasets.
Computational Methods for High Resolution 3D Fluorescence Microscopy
BioImage Analysis Community Conference (NEUBIAS), Lisbon, Portugal, February 12-17, 2017.
Epifluorescence microscopy results in blurry images with a very coarse optical sectioning; this limits its usefulness for cellular imaging, where resolving subcellular structures requires resolution close to or even beyond Abbe's diffraction limit. Techniques such as confocal microscopy (CLSM) and selective plane illumination microscopy (SPIM) have been proposed to reduce the out-of-focus light and to improve the resolution. Several other modalities, such as single-molecule localization microscopy (SMLM) and structured illumination microscopy (SIM) make the use of multiple acquisitions, trading time for resolution. These last techniques have been recently extended to 3D imaging.
Open Software Tools for Microscopy Image Processing
International ELMI Meeting (ELMI), Debrecen, Hungary, May 24-27, 2016.
Recent advances in microscope technology combined with new digital tools now provide outstanding images (3D, time-lapse, multichannel, fluorescence), allowing us to address fundamental questions in developmental biology, molecular biology and neuroscience. The analysis of this unprecedented flow of imaging data requires the development of sophisticated software packages to numerically reconstruct images and to automatically perform segmentation, quantification and tracking of structures of interest. This has led to the emergence of a new field of research, #bioimage informatics,# which aims to develop computational procedures to process, analyze, and visualize images coming from various light microscopy techniques. Here, we report our experience in the development of open-source software tools. These tools are written as Java plugins for the popular software suites: ImageJ, Fiji or Icy. In particular, we are focusing on the reconstruction of images from incomplete data measurements. This is often a challenging image-processing task in terms of algorithmic tuning and computational runtime. In this context, we show the importance of carefully identifying the image formation model in properly designing algorithms. We describe bioimaging applications such as restoration of details with deconvolution methods, recovery of shape from phase images, segmentation of cellular compartments from the photobleaching decay, reconstruction of nanoscale images by applying super-resolution localization microscopy, and generation of theoretical point-spread functions. Often overlooked in software development, the validation and usability are finally what counts for the end-users. In this respect, we report our effort to propose reference datasets and quantitative benchmarks of software through the organization of Grand Challenges.
Bioimage Informatics: Image-Processing Algorithms and Life-Science Applications
International Neuroinformatics Coordinating Facility (INCF), Antwerp, Belgium, September 2-6, 2013.
In this presentation, we first give an overview of the underlying concepts of the image processing including: basic elements of digital signal-processing theory, pixelwise operations, digital filtering and an introduction to image analysis. Then, we show how the image-processing algorithms can be efficiently implemented as Java plugins for the open-source platform ImageJ/Fiji for the benefit of the research community. We cover the image preparation (correction for drift by intensity-based registration, correction for photobleaching, correction for non-uniform illumination), the image restoration (extended depth-of-field procedure, denoising, 3D microscopy deconvolution), the image analysis (feature identification, segmentation, active contour, directional image analysis) and tracking of biological fluorescent particles (spot tracking). The presentation includes intuitions and concepts of algorithms, their implementations running as ImageJ/Fiji plugins and applications to biological microscopic images. This is joint work with members of the Biomedical Imaging Group at the EPFL who have contributed to this effort over the years.
Analysis in Live Cell Imaging—ImageJ/Fiji Solutions
International Symposium in Applied Bioimaging (ISAB), Porto, Portugal, September 20-21, 2012.
ImageJ and its distribution Fiji are widely-used public-domain software for live cell imaging applications. The open architecture of ImageJ/Fiji provides extensibility via recordable macros and Java plugins; this simulates the creativity of the developers and facilitates the dissemination of algorithms to the biological community. ImageJ, the defacto standard in bioimage software, federates people from different fields: biology, neuroscience, imaging science, microscopy, computer science in a community promoted by a new group: the Open Bio Image Alliance. In this context, we present a collection of image-processing algorithms useful for microscopy and biological applications. The presentation includes intuitions and concepts of algorithms, their implementations running as ImageJ plugins and an application to biological microscopic images. We cover the image preparation (correction for drift by registration, correction for photobleaching, correction for non-uniform illumination), the image restoration (extended-of-field procedure, denoising of fluorescence images, 3D microscopy deconvolution by PSF modelling), the image analysis (feature identification, segmentation by parametric active contour, directional image analysis) and tracking of biological particles (spot tracking). Most of the plugins have been developed by researchers of the Biomedical Imaging Group at the EPFL during the past ten years. We have made them freely available and accessible to end-users.
Signal-Processing Algorithms for Bioimaging
Int. Conf. on Signal Processing and Communications (SPCOM), Bangalore, India, July 18-21, 2010.
In the last decade, images have become indispensable to understand the structure of the cell organisms and revealing their dynamic interactions. Imaging often plays a key role in discoveries in biology. Structures or particles of interest are tagged with fluorescent probes and imaged with a new generation of 3D high-resolution microscopes which produce a large amount of data for quantitative analysis. Automatic processing of these microscopic images remains a challenging task for the image-processing community, one has to handle multidimensional data often corrupted by a defocussing effect, non-uniform lightning, or important noisy and to deal with living particles that rapidly move, grow, interact, or divide. In this tutorial, we describe several algorithms the restoration and analysis of microscopic images of biological organisms. These algorithms are implemented as Java plugins for ImageJ which is the most popular public-domain image-processing software package in the field of bioimaging. We cover the image preparation (correction for drift by registration, correction for photobleaching, correction for non-uniform illumination), the image restoration (extended-of-field procedure, denoising, deconvolution by PSF modelling) and image analysis (feature identification such as spots or filaments, segmentation by parametric active contour, directional analysis, and tracking). These algorithms have been developed by the Biomedical Imaging Group of the EPFL. While they are based on solid signal-processing fundaments, we have made them freely available and accessible to end-users. The presentation includes concepts of algorithms, applications to life cell imaging and live demonstrations.
Results: We introduce Steer’n’Detect, an ImageJ plugin implementing a recently published algorithm to detect patterns of interest at any orientation with high accuracy from a single template in 2D images. Steer’n’Detect provides a faster and more robust substitute to template matching. By adapting to the statistics of the image background, it guarantees accurate results even in the presence of noise. The plugin comes with an intuitive user interface facilitating results analysis and further post-processing.
Daniel Sage was born in Annecy, France. He received the Master degree and Ph.D. degrees in signal and image processing from the Institut National Polytechnique de Grenoble INPG, France. He did his research Ph.D. thesis at the GIPSA laboratory (previously TIRF) on tracking methods. From 1989 to 1998, he was a Consulting Engineer developing vision systems for quality control, then Head of the Industrial Vision Department of Attexor S.A. During his career, he has developed some vision systems oriented to the quality control in the industrial sector.
In 1998, Daniel Sage joined the Biomedical Imaging Group (BIG) of the Prof. M. Unser at Ecole Polytechnique Fédérale de Lausanne (EPFL) as responsible of the Head of the Software Development. He is currently in charge of the support to the researchers of the laboratory and also to the research community of the EPFL Center for Imaging. He is involved in numerous research projects in computational bioimaging including super-resolution microscopy, tracking, deconvolution, and image quantification. He is engaged in the open-source software development for the life science community, using both engineering and machine learning methods. He is also involved in the teaching of image processing and image analysis, including the development of methods for computer-assisted teaching.