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STUDENT PROJECTS

Proposals

On-going Projects

Completed Projects

Conditions : The Bachelor Semester Projects and Master Semester Projects are only reserved for regular EPFL students or for students of enrolled in am official mobility program.

Project Proposals

All Project

Number of projects:19

Tracking animal behavior in neuroimaging studies

Master Diploma Project: Available

Insects exhibit robust terrestrial locomotion while relying on a relatively small and simple controller, the nervous system. This makes it an ideal source of bio-inspiration for robust legged walking robots. The main objective of this project is to develop an algorithm for extracting the leg positions of tethered walking flies in high-resolution movies. Ultimately, the goal is to perform a quantitative analysis of their behavior to link with simultaneously acquired neuroimaging data. This new knowledge can be applied toward inspiring more efficient and robust robotic control algorithms. The task of the student will be to design and implement a suitable algorithm for segmenting fly legs in the images of interest. This will involve searching the literature for mathematical models of leg joints in insects and investigating whether these can be used to constrain active contours for better fitting results. The segmentation method will then be incorporated into a complete tracking framework for processing video sequences. Finally, biologically relevant data will be extracted. The project is at the interface between engineering, image analysis, and neurobiology. It will primarily be supervised at the Biomedical Imaging Group, in close interaction with the Neuroengineering Laboratory (EPFL).

Supervision:

Application of Novel Dictionary Learning Framework

Bachelor Semester Project or Master Semester Project: Available

Dictionary learning (DL) methods have gain major popularity over the past year thanks to their ability to represent data with a concise base of basic elements. We have developed a DL framework based on sparse distribution tomography. The parameters of the underlying model are recovered using a new type of probability distribution tomography. The goal of this student project would be to apply the framework for the denoising and/or deconvolution of biomedical images, and to assess its performance when compared to popular DL libraries.

Supervision:

Two dimensional SIM reconstruction from 4 images

Master Semester Project: Available

Structured Illumination Microscopy (SIM) allows us to improve the resolution of classical wide-field imaging systems by moving high-frequency components into the observable microscope region. When dealing with 2D data, one generally requires 9 patterned images to reconstruct a super resolved image. Reducing this number of images is essential in order to improve temporal resolution of the system. The first part of this project will consist in showing properly that 4 patterned images are in fact sufficient to reconstruct a super resolved image (consistent system of equations). Based on this analysis, the second part of the project will be devoted to the development of a direct algorithm (non-iterative) requiring only 4 input images.

Supervision:

Semi-blind reconstruction for Structured Illumination microscopy

Master Semester Project or Master Diploma Project: Available

Structured Illumination Microscopy (SIM) allows us to improve the resolution of classical wide-field imaging systems by moving high-frequency components into the observable microscope region. This microscopy technique relies on patterned illuminations of the sample to produce super-resolution images. As a result, pattern calibration conditions the reconstruction performance and reconstruction artefacts are often due to an inaccurate knowledge of these patterns. However, patterns are generally partially known up to a parametric model whose parameters remain unknown. The objective of this project is thus to develop a reconstruction algorithm in order to jointly estimate patterns model parameters and the super-resolved volume. To this end, the student will benefit from the Matlab inverse problem library developed in out group (http://bigwww.epfl.ch/algorithms/globalbioim/).

Supervision:

Benchmarking of numerical methods for solving inverse problems

Master Semester Project: Available

Inverse problems are at the heart of many microscopy and medical imaging modalities where one aims at recovering an unknown object from given measurements. Such a problem is generally addressed through the minimization of a given functional composed of a data-fidelity term plus a regularization term. Within the Biomedical Imaging Group, we are currently developing a Matlab library (http://bigwww.epfl.ch/algorithms/globalbioim/) unifying the resolution of inverse problems. This library is based on several blocks (forward models, data-fidelity terms, regularizers, algorithms) that can be combined to solve any inverse problem. Given an imaging modality, one can thus easily compare methods that use different data terms, regularizers or algorithms. The goal of this project is to develop a Matlab code which, for a given modality, outputs in an elegant way different metrics showing the performances obtained using all the combinations of blocks (forward models, data-fidelity terms, regularizers, algorithms) that are available within the Library.

Supervision:

Rotation axis estimation for parallel-beam X-ray CT

Bachelor Semester Project or Master Semester Project: Available

In parallel-beam X-ray CT, a 3D image of an object is reconstructed from a collection of its 2D X-ray projections taken from different angles around a fixed rotation axis. The location of the axis must be estimated accurately or serious artifacts arise during reconstruction. In this project, the student will implement a method (already existing in the literature) for estimating the rotation axis directly from the data and compare its performance to a baseline method on simulated and real data.

Supervision:

Active Contour for Jointly Segmentation of Multiple Cells

Master Semester Project or Master Diploma Project: Reserved

Active contours are powerful methods for the segmentation of biomedical images. However, they usually segment only well cell at a time, which makes fastidious the segmentation of microscopic images that often contain hundreds of cells. The goal of this project is to develop an active contour that is able to jointly segment multiple cells. We will work on fluorescence images of the embryo of c-Elegans. In fact, the study of early cell division is an active field of research in developmental biology. Good notions on image processing and optimization are strongly advised, and experience with java is recommended.

Supervision:

Optimal Configurations for Parallel-Beam Computed Tomography

Master Semester Project: Reserved

Parallel-beam computed tomography aims at reconstructing the 3D volume of an object from its 2D projection measurements. As most rays used in CT imaging (x-rays, electron-rays, etc.) are harmful for the object being imaged, an important issue is to maximally reduce the radiation dose necessary for high-quality 3D reconstructions. In this project, we will study, through 2D and 3D simulations, the impact of two dose-reduction approaches (i.e., reducing the number of tilt views Vs reducing the dose per view) on the quality of the reconstructed image, at different levels of gaussian noise. This shall be done for two distinct types of reconstruction algorithms: filter-back projection (FBP) algorithms and more advanced iterative algorithms.

Supervision:

Shape analysis of C. elegans datasets using dictionary learning

Master Diploma Project: Available

Sparse dictionary learning is a powerful approach for revealing patterns in datasets. The idea of this project is to transpose the concept to the representation of parametric curves with, as a goal, the description of shape variability and motion types of C. elegans worms. The input data will consist of video sequences of swimming C. elegans worms, where the worm contours are outlined by spline curves in each individual image. The shape dictionnary will then be constructed considering this large collect of spline curves and some sparsity constraints. This should provide a new efficient way to describe the shape and motility of worms and help in the identification of particular phenotypes in a high-throughput screening context. Good notions on the theory of signal and image processing are strongly advised, and experience with Matlab/Mathematica is recommended.

Supervision:

3D Steerable Filter Learning for Efficient Volumetric Image Analysis

Master Semester Project or Master Diploma Project: Reserved

The use of deep convolutional neural networks (CNN) for object recognition in computer vision has shown to provide excellent results in many applications. Deep CNNs learn multiple filters in each convolutional layer of a deep neural network architecture using backpropagation weight updates. A major drawback of the latter is the requirement of large amounts of training data and computational time to learn all pixel weights (i.e., free parameters) of the filters. Moreover, CNNs are not rotation-invariant and require extensive re-training with augmented data (e.g., rotated versions of the training images), which degrades the specificity of the learned filters. Steerable filters are used on image analysis as efficient and accurate rotation-invariant object detectors. They are excellent candidates to overcome these drawbacks. The 2D theory has been recently adapted to classification problems and applied to texture analysis. The goal of this project is to extend the framework to the 3D setting, where rotation-invariance is even more important. This presents both mathematical and implementation challenges.

Supervision:

Convergence of Discretized TV-Regularization Schemes to L-splines

Master Semester Project or Master Diploma Project: Available

We have recently shown that any generalized TV-regularization problem in continuous-domain is minimized by a non-uniform spline whose knot locations are not fixed a priori. This result was exploited to design new spline-based algorithms which are able to reconstruct spars e signal from their noisy measurements. The strategy we follow is to discretize the real axis into a uniform grid and to find the optimal spline with knots on the grid. We expect the procedure to converge to the optimal spline when the grid gets finer and finer. The goal of this project is to provide theoretical and/or experimental evidence of this convergence.

Supervision:

Slowly Growing Poisson Processes

Master Semester Project: Available

Poisson processes are used to model sparse and piecewise-smooth signals. They are pure jump processes characterized by the law of the jumps and the average density of knots. The properties of the law of the jumps is intimately linked with the asymptotic behavior of the process. The goal of this project is to prove that a Poisson process is slowly growing (that is, bounded by a polynomial) if and only if the law of jumps has some finite moment. This result has been proven very recently with advanced technics, but we aim now at obtaining a short and relatively elementary proof. The student should have strong mathematical interests, with basic knowledge on probability theory and functional analysis.

Supervision:

Deep Learning for Image Inpainting

Master Semester Project or Master Diploma Project: Reserved

Image inpainting recovers missing information within images, for instance the data underlying blemishes from antique pictures. Another use is to counteract photo bombers by allowing for the erasure of unwanted elements. The most recent inpainting methods rely on various optimization methods, in particular, on iterative convex optimizers. However, this type of optimization methods are computationally demanding. Nowadays, convolutional neural network (CNN) are becoming a popular solver of various inverse problems in the framework of supervised deep learning. It is observed that the performance and speed of reconstruction of CNN are noticeably improved compared to conventional iterative optimization methods. However, until now, there is a lack of CNN approaches for image inpainting. The student's task will be to explore a CNN-based reconstruction method. The implementation will take advantage of the MatConvNet toolbox of MATLAB.

Supervision:

B-spline implementation to find the solution of continuous domain total-variation minimization problem

Master Semester Project: Reserved

In MRI and other real world applications, the measurements are generally obtained through a continuous-domain transformation of a continuous-domain signal (Fourier samples for MRI case). Yet, for computational feasibility, the inverse problem formulated to numerically reconstruct the signal from these measurements, are often formulated in discrete-domain. Continuous-domain formulation of inverse problems therefore can be advantageous in this sense, provided there is a way to tackle the computational complexity of the reconstruction task. Recently in [1], the solution for inverse problems in continuous domain with Total variation regularization is found out to be non-uniform spline. To perform the reconstruction we use Green's function of the operator used in regularization, as the dictionary basis. However, this often results in ill-conditioned system matrices leading to poor convergence rate. We propose a student project to use the corresponding B-splines as the dictionary basis for the TV-regularized solution. The resultant system matrix in this case is expected to be better conditioned and an appropriate algorithm can result in faster convergence to a solution. The task will be to effectively implement this formulation and contrast it with the results of the previous formulation. The student will have to understand the theoretical background of the problem and convex optimization techniques, and then implement the formulation in MATLAB. Prerequisites: Convex optimization [1] M. Unser, J. Fageot, and J. P. Ward, “Splines are universal solutions of linear inverse problems with generalized-TV regularization,” arXiv preprint arXiv:1603.01427, 2016.

Supervision:

Learning Approach for Image Restoration

Master Semester Project or Master Diploma Project: Reserved

l1-minimization has proved a powerful tool for image restoration. In this framework, the degraded image is iteratively deblurred in the space-domain and denoised with a soft thresholding in some transform-domain in which the original image is supposed to be sparse. In this project, we will adopt a learning approach to optimize the denoising step. Instead of using a soft thresholding, we learn a nonlinear shrinkage function from a collection of images and their synthetically degraded versions. The sparsifying transform can also be learned to improve the performance of the restoration. You are supposed to implement a learning algorithm for some reconstruction method such as total variation. Prerequisites: a good knowledge of image processing, linear algebra, a little vector calculus, and a little nonlinear optimization.

Supervision:

Building Experimental 3D Point-Spread Function (PSF) for Super-Resolution Microscopy

Master Semester Project: Available

In fluorescence microscopy, it is of utmost importance to get the 3D point-spread function (PSF) for several image-processing, like deconvolution, super-resolution reconstruction of images, or single-molecule localization microscopy. The PSF give the answer of the optical system to a pont source, it characterizes the image formation model. To get a PSF, microscopists generally acquire a z-stack of a field of fluorescence small beads (eg. 100nm) and then pick up the good ones to average them. The drawback of this method is to create blurred PSF. In this project, we propose to design and to implement a image-processing module to select the "good" beads and to perform accurate localization first before to reconstruct a sharp PSF. The module should be enough flexible to handle various engineering PSF, like the astigmatism PSF or the double-helix PSF. The module will be a Java plugin of ImageJ or Icy.

Supervision:

Blind deconvolution for high resolution microscopy

Master Semester Project: Available

In the last decades, optical microscopy has made huge steps toward high spatial resolution reaching now few 10s nm scale. These super-resolution has been made possible by intensive research in optics and fluorochrome engineering, and there is still room for improvements by careful data processing. In this project, the student will extend the blind deconvolution algorithm we have previously developed for wide field microscopy to higher resolution modalities as confocal, two-photons and light-sheet microscopy. The code will be developed in JAVA to be used as a plugin in the Icy software.
Check the demonstration on You tube.

Supervision:

Image-based quantification of cell blebbing

Master Semester Project: Available

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.

Supervision:

Detection of nanoparticles in scanning tunneling microscopy images

Master Semester Project or Master Diploma Project: Available

One of the applications of scanning tunneling microscopy (STM) is the inspection of ligand-shell structures of gold nanoparticles. It is often required that a large number of STM images under different imaging conditions must be collected and analyzed to ensure an objective interpretation. Consequently, there is a crucial need for image-processing methods, e.g., for restoring images, modeling structures, or detecting features automatically. Interested students will develop robust image-processing procedures for STM images with an aim to derive meaningful information needed for an unbiased subsequent image interpretation.

The figure to the right shows a schematic drawing of the STM imaging process, as well as a sample image. One of the (challenging) objectives of this project would be to detect the ribbon-like domains on each nanoparticle, which appear as very faint stripes in the image.

Supervision:

2017 EPFL • webmaster.big@epfl.ch05.09.2017