Description
Internship project:
Astral microtubules are dynamic and semi-flexible fibres.
Their dynamics were studied in vivo, which revealed their major role in cell division, and in particular concerning astral microtubules in the correct positioning of the mitotic spindle [1-2].
In contrast, the mechanical characteristics of microtubules in vivo are not well known, and their contribution to cell division remains to be identified.
Microtubule curvatures can serve as proxy for revealing perturbations in microtubule rigidity.
The team as already developed a Deep-learning-based tool capable of segmenting curvilinear structures, including microtubules, in noisy 2D images.
For that we build a U-Net architecture that combines residual networks and attention mechanisms [3].
In the present internship, we aim add developing a similar tool that will succeed to segment microtubules in 3D images, and even to go one step further by directly extracting microtubule curvatures.
By developing deep-learning based tools for 3D microscopy data, the student will help understand how changes in microtubule rigidity influence cell division, while gaining hands-on experience in designing and applying state-of-theart deep-learning methods for complex biomedical image analysis.
References:
[1] Bouvrais, H., et al., Biophysical Journal, .
: p.
- .
[2] Bouvrais, H., et al., EMBO reports, .
22: p.
e.
[3] Ait Laydi, A., et al., arXiv preprint arXiv:.,
Duration: From 2 to 6 months
Period: Between January and July .
Contact:
Dr Sidi Mohamed Sid’El Moctar (post-doctoral fellow)
Dr Hélène Bouvrais (CNRS Researcher)
Web site:
Profile
Skills:
- Required: Solid programming skills in Python; Basic understanding of deep learning architectures (e.g., CNNs, U-Net).
- Preferred: Interest in biological applications and ability to work in an interdisciplinary environment; Knowledge in image analysis.
Skills that will be acquired: Image analysis, deep-learning, microscopy, Improvement in English, teamwork skills.
Starting date
Dès que possible