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Annonce

10 avril 2020

PhD position: Registration of multiple point clouds in a deep learning framework. Application to single molecule localization microscopy


Catégorie : Doctorant


PhD subject: Registration of multiple point clouds in a deep learning framework. Application to single molecule localization microscopy

Location: ICube laboratory, IMAGeS team, University of Strasbourg

Starting date: September/November 2020

Supervisors:

  • Denis Fortun (dfortun@unistra.fr)
  • Sylvain Faisan (faisan@unistra.fr)

Profile of the candidate

  • Master diploma in one of the following fields: computer science, applied mathematics, machine learning
  • Good programming skills (the coding language will be Python)
  • Interest for biomedical applications

Application before May 8th: Send a CV and a description of your motivation, as well as the transcript of marks for the past 2 years to Denis Fortun (dfortun@unistra.fr) and Sylvain Faisan (faisan@unistra.fr).

Link to the detailed description

 

Summary:

Single molecule localization microscopy (SMLM) is one of the most powerful and widely used fluorescence imaging techniques. To push further the resolution, the goal of this thesis is to develop single particle reconstruction (SPR) methods for SMLM. The principle is to reconstruct a single particle that fuses information from several of its replicates imaged at random orientations. Recently, SPR has been successfully investigated for several fluorescence imaging modalities. However the data provided by SMLM is in the form of point clouds and requires the development of a dedicated registration method. Traditional approaches for point clouds registration in computer vision have revealed critical limitations to deal with the specific features of our SMLM data, namely (i) strong anisotropic localization uncertainty associated to each point, (ii) very high number of particles to be registered jointly, and (iii) symmetry priors on the shape of the particles. To overcome this issue, the methodological approach of the thesis will be based on a deep learning framework, which has been emerging in recent years for point clouds processing and offers promising perspectives. The PhD student will have to develop innovative methods in this framework to handle the aforementioned issues of SMLM data. The project will be carried in close collaboration with biologists of the University of Wurzburg, to guarantee the access to real data and a relevant biological application.

 

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