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Annonce

15 octobre 2020

stage M2 : Super-resolution fluorescent microscopy using GANs


Catégorie : Stagiaire


M2 internship proposal for spring 2021 (Duration: 5/6 months)

Title: Super-resolution fluorescent microscopy using GANs

Supervisors: Laure Blanc-F´eraud (blancf@i3s.unice.fr), Luca Calatroni (calatroni@i3s.unice.fr),
Sebastien Schaub (sebastien.schaub@imev-mer.fr)

Host institution: MORPHEME research group (INRIA, CNRS, I3S, Sophia-Antipolis, France)

 

M2 internship proposal for spring 2021 (Duration: 5/6 months)

Title: Super-resolution fluorescent microscopy using GANs

Supervisors: Laure Blanc-F´eraud (blancf@i3s.unice.fr), Luca Calatroni (calatroni@i3s.unice.fr),
Sebastien Schaub (sebastien.schaub@imev-mer.fr)

Host institution: MORPHEME research group (INRIA, CNRS, I3S, Sophia-Antipolis, France)

Context
Conventional optical microscopy techniques, as confocal microscopes, are widely used in biology for cellular
and sub-cellular structures investigation. However, their spatial resolution is limited by the light diffraction
phenomena and it is typically around 200nm in the transverse plane and 400nm in the optical axis. Over the
recent years, several super-resolution techniques have been developed to overcome this drawback. Popular
techniques, such that, for example, SMLM (Single Molecule Localization Microscopy) are based on the use
photoactivable molecules, others on structured illumination or on the analysis of the stochastic fluctuation
of molecules (such as SOFI) and many more [1]. In the Morpheme group we have developed advanced
algorithms for SMLM and for the analysis of SOFI-type images based on sparse optimization reconstruction
methods, see, e.g. [2, 3]. The super-resolved image reconstruction is formalized in mathematical terms as
an inverse problem which is regularized by introducing a sparsity-promoting penalization. A different and
increasingly popular class of methods producing outstanding results in many applied fields is based on the
use of modern Deep Learning tools. Among them, Generative Adversarial Networks [4] have attracted the
attention of the inverse problem community in recent years [5]. Their use in the field of image microscopy
remains limited.
Internship objectives
The purpose of this internship is to develop an inverse super-resolution method based on the use of GANs
[4, 5]: the idea is to develop a model capable of finding both the desired image and the parameters of the
formation model by minimizing a suitable distance between the distributions of the real images acquired
by the microscope and the images synthesized by the model. This approach has already shown promising
results in the framework of applied inverse problems, see, e.g., [6]. Its performance will be compared with
the methods already developed in the Morpheme research group on synthetic and real data.


Candidate profile
Second year of Master degree in computer science, applied mathematics, data science with background
in image processing, imaging inverse problems, deep learning and optimisation. Good coding skills for
numerical simulation (Pytorch, Python, MATLAB, ...). A general interest in health and biology is welcome.
Practical information
MORPHEME research team is a joint research group between INRIA Sophia Antipolis M´editerran´ee., I3S
Lab (Universit´e Cˆote d’Azur and CNRS).
Remuneration: internship gratification (approximately 550 euros/month) and possible discounts for nearby
accommodation facilities (CIV).
Application procedure
Please send your CV, motivation letter, marks of the last two years of study and the name and e-mail
address of a contact for recommendation to Laure Blanc-F´eraud (blancf@i3s.unice.fr), Luca Calatroni (calatroni@
i3s.unice.fr).

References
[1] L. Schermelleh, A. Ferrand, T. Huser, C. Eggeling, M. Sauer, O. Biehlmaier, and G. P. C. Drummen,
“Super-resolution microscopy demystified,” Nature Cell Biology, vol. 21, no. 1, pp. 72–84, 2019.
[2] S. Gazagnes, E. Soubies, and L. Blanc-F´eraud, “High density molecule localization for super-resolution
microscopy using CEL0 based sparse approximation,” in 2017 IEEE 14th International Symposium on
Biomedical Imaging (ISBI 2017), pp. 28–31, 2017.
[3] J. H. de M. Goulart, L. Blanc-F´eraud, E. Debreuve, and S. Schaub, “A study on tensor and matrix
models for super-resolution fluorescence microscopy,” in 2019 IEEE 8th International Workshop on
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 321–325, 2019.
[4] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio,
“Generative adversarial nets,” in Advances in Neural Information Processing Systems 27 (Z. Ghahramani,
M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, eds.), pp. 2672–2680, Curran
Associates, Inc., 2014.
[5] S. Lunz, O. ¨ Oktem, and C.-B. Sch¨onlieb, “Adversarial regularizers in inverse problems,” in Advances in
Neural Information Processing Systems 31 (S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-
Bianchi, and R. Garnett, eds.), pp. 8507–8516, Curran Associates, Inc., 2018.
[6] H. Gupta, M. T. McCann, L. Donati, and M. Unser, “CryoGAN: A New Reconstruction Paradigm for
Single-particle Cryo-EM Via Deep Adversarial Learning,” bioRxiv, 2020.

 

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