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STUDENT PROJECTS

Proposals

On going

Completed

Conditions:The projects are reserved for EPFL students or students of mobility program.

Ongoing projects

All Project

Number of projects:8

Optimal Configurations for Parallel-Beam Computed Tomography

Martial Bernard-Michel
Bachelor Semester Project or Master Semester Project or Master Diploma Project: Summer 2018

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:

Tracking animal behavior in neuroimaging studies

Julien Rüegg
Master Diploma Project: Summer 2018

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:

Steerable filters as imaging biomarkers for precision medicine

Mickaël Salamin
Master Semester Project: Summer 2018

Radiological data are massively produced in hospitals and are currently underexploited due to the limitation of radiologists to exhaustively and quantitatively analyze them. Recent promises of machine learning showed the ability of computerized algorithms to complement the work of radiologists and to provide powerful and non-invasive imaging biomarkers of lung cancer malignancy. Among them, steerable filters have strong advantages over classical methods, as they can deliver rotation-invariant analysis of biomedical tissue at a low computational cost. In this project, the student will design a lung nodule classification pipeline based on steerable filters and machine learning to automatically classify them as benign versus malignant, and compare it with existing approaches.

Supervision:

Deep convolutional neural networks for precision medicine

Roser Viñals
Master Semester Project: Summer 2018

Radiological data are massively produced in hospitals and are currently underexploited due to the limitation of radiologists to exhaustively and quantitatively analyze them. Deep convolutional neural networks showed tremendous performance in the analysis and recognition of objects in natural images, but the existing frameworks are not well adapted to medical image analysis. In this project, the student will design a lung nodule classification pipeline based on deep learning to automatically classify them as benign versus malignant, and compare it with existing approaches.

Supervision:

Web tools for image processing and for super-resolution microscopy visualisation

Robin Lang
Master Semester Project: Summer 2018

Supervision:

Semi-blind reconstruction for Structured Illumination microscopy

Rémy Gardier
Master Semester Project or Master Diploma Project: Winter 2017

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:

Restoring axial resolution using a 2D/3D deep convolution neuronal network

Joey Zenhäusern
Master Semester Project: Summer 2017

In 3D fluorescence microscopy, the axial resolution (axis Z) is often lower than the lateral resolution (optical plane XY). This non-isotropic resolution penalizes the global resolution of the biological structures. The conventional way to expand the size is to interpolate in the Z axis which create blurry 3D images without recovering any structure. In this project, we propose to recover the axial resolution based on the deep learning method. We propose a 2D strategy by patches to build a reasonable system. The learning will be performed in the 2D XY plane (higher resolution) than the reconstruction in performed in the 2D XZ or/and in the YZ planes. The learning with be done using a deep convolution neuronal network (CNN) in particular using a multiresolution approach with the U-net architecture. This recent architecture has demonstrated very good results in many fields.

Supervision:

Deep neural networks: learning with splines

Arnaud Pannatier
Master Semester Project or Master Diploma Project: Summer 2017

A recent paper (Poggio et al, 2015) points out that deep neural networks with RELU activation functions can be interpreted as hierarchical splines. The purpose of this project is to exploit this connection in order to gain further understanding and to improve the performance of such networks. Following a formal statement of the problem, the project will consist in an extensive (but informed) experimental comparison of different network configurations in order to determine the most promising one. The idea is to keep the number of parameter fixed (total number of RELUs and linear weights) and to investigate the effect of the architectures-in particular, the number of layers-on the prediction error. This project gives an excellent opportunity to deeply understand fundamental aspects of deep learning networks.

Supervision:

2018 EPFL • webmaster.big@epfl.ch05.06.2018