Number of projects:7
Particle fields include a large range of samples of interest, such as bubbles, droplets, or biological cells.
To obtain a three-dimensional (3D) volume of such fields, one popular method involves in-line digital holography (DH).
In this imaging modality, the particle field is illuminated with an incident field (light) so that multiple scattering and diffraction occur. The resulting field is then holographically recorded.
From a single two-dimensional (2D) DH image, computational methods are able to recover the particles within a 3D volume. When the density of particles and/or the depth of field are large, the reconstruction task becomes too difficult for conventional methods.
During this project, the student will implement and train a neural network to recover particles within a 3D volume from a 2D image. The programming language is Python (Pytorch). Based on an existing code in Matlab, the student will also implement the physical model which describes the wave propagation in Pytorch. The required skills are prior knowledge of deep learning, proficiency in coding in Pytorch. The student should be able to learn the basics of wave propagation and optics during the project.
During this project, the student will understand the physical model of an imaging modality, learn how to conduct a complete project with deep learning, and learn how to use a physical model combined with deep learning.
References Tahir, W., Kamilov, U. S., & Tian, L. (2019). Holographic particle localization under multiple scattering. Advanced Photonics, 1(3), 036003.
In computed tomography (CT), the goal is to reconstruct a 3D object from a set of its 2D projections. Typically, this reconstruction task is formulated as an optimization problem where one exploits certain properties of the signal of interest (e.g., sparsity in a transform domain). However, over the past decade, several learning-based methods have been shown to outperform the classical reconstruction methods. In this project, we consider a setting where relatively fewer projections are available and the idea is to use generative adversarial networks (GANs) for the reconstruction task. The student should be familiar with the PyTorch framework and should have a general understanding about basic deep learning concepts. Prior experience in inverse problems and/or optimization is a definite plus.
References:  H. Gupta, K.H. Jin, H.Q. Nguyen, M.T. McCann, M. Unser, "CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction"  A. Bora, A. Jalal, E. Price, A. G. Dimakis, "Compressed sensing using generative models"
Single-particle cryo-electron microscopy (cryo-EM) has revolutionised the field of structural biology over the last decade, culminating in 2017 by the awarding of the Nobel Prize in Chemistry to its three founders. Nowadays, single-particle cryo-EM permits the regular discovery of new biological structures at atomic resolution. Yet, the reconstruction task remains an enduring challenge due to the unknown orientations adopted by the 3D particles prior to imaging. The goal of this project is to further strengthen a recently-developed joint optimization scheme that efficiently alternates between the reconstruction and the estimation of the unknown orientations . More precisely, the student will introduce a multiscale scheme  inside the iterative-refinement framework itself to benefit from the robustness gained by reconstructing volumes at coarser scales. The student should have a strong interest in image processing, and good Matlab skills are a prerequisite. An interest in inverse problems and/or optimization theory is a definite plus. References:  M. Zehni, L. Donati, E. Soubies, Z. Zhao, M. Unser, "Joint Angular Refinement and Reconstruction for Single-Particle Cryo-EM," IEEE Transactions on Image Processing, vol. 29, pp. 6151-6163, 2020.  L. Donati, M. Nilchian, C.Ó.S. Sorzano, M. Unser, "Fast Multiscale Reconstruction for Cryo-EM," Journal of Structural Biology, vol. 204, no. 3, pp. 543-554, December 2018.
Inverse problems with l1 regularization are popular method for signal reconstruction. This is due to the fact that they promote sparse solutions, i.e., with few nonzero coefficients, and the observation that many real-world signals are sparse in a certain basis. However, due to the non-differentiability of the l1 norm, such problems do not admit a close-form solution; they are thus typically solved using iterative algorithms based on the proximal operator of the l1 norm. In this project, we propose to benchmark two of these proximal algorithms, the standard and very popular alternating direction method of multipliers (ADMM) , and a primal-dual splitting algorithm introduced by Condat . The goal will be to compare the performance of these algorithms in various settings. The student should have a strong interest in optimization.
 Boyd, Stephen, et al. "Distributed optimization and statistical learning via the alternating direction method of multipliers." Foundations and Trends® in Machine learning 3.1 (2011): 1-122.
 Condat, Laurent. "A primal–dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms." Journal of Optimization Theory and Applications 158.2 (2013): 460-479.
High speed imaging can be achieved with any camera, as has recently been demonstrated with the Virtual Frame Technique. Using this method, any monotonic phenomenon that is imaged instantaneously with perfect contrast (such that the image is binary) can be recorded at high rates by increasing the exposure time of the camera. Thus, complex temporal dynamics can be recorded at high rates and high resolution, averting the traditional trade-off between size of the region of interest and imaging rate. In this project we explore the limits of this method, using a dual-approach of experimental demonstration and theoretical analysis. Project in collaboration with Engineering Mechanics of Soft Interfaces (EMSI) laboratory (https://www.epfl.ch/labs/emsi/).
Classification and clustering are some of the most important objectives in supervised and unsupervised learning, respectively. Interestingly, in both scenarios, the learning scheme eventually produces a piecewise-constant function. This remarkable property allows one to analyze them jointly. The goal of this project is to develop a variational framework to estimate piecewise-constant functions and to derive an efficient learning algorithm, built as a module. One can then also use this module in deep neural networks and compare the performance with classical setups for various applications of classification and clustering.
Blebbing is a very dynamic phenomenon that plays an important role during apoptosis, cell migration, or cell division. Using time-lapsed microscopy techniques, phase contrast and fluorescence, biologists can observe blebs which are spherical protrusions which appear and disappear on the membrane of the cell. The goal of the project is to design and to implement image-analysis algorithms based on active contour and curve optimization take into account the blebbing. It requires a automatic segmentation of the cell over the multichannel sequence of images and a local extraction of the bulges to quantify blebbing. The project will be implemented in Java as an ImageJ plugin with an user interface allowing a manual edition of the outlines of the blebs.