3D imaging of historical buildings

When it comes to generating 3D digital geometric models of historical buildings, the automation of methods is still limited. Existing research focused on sacral structures, on 3D model generation of the exterior envelope of buildings and on segmentation of interior spaces. The goal of this project is (i) to develop a data acquisition and post-processing pipeline for deriving the exterior and interior geometry of historical buildings in terms of 3D point clouds derived from spherical RGB images, (ii) to augment this data with information extracted from historical architectural drawings, and (iii) to approximate the 3D point clouds by geometrical primitives describing the architectural and structural elements in historical buildings. Because interior spaces are often very privacy-sensitive spaces, privacy-issues will be considered from the start, limiting the stored data to data containing architectural and structural elements. The derived models can be used for a wide range of research applications, such as 4D modelling showing the evolution of a historical building over time and structural modelling of historical buildings, which requires as input a geometric model of the building.

Spatiotemporal adaptive microscope control, driven by biological events

A key tool for studying the dynamics of living systems is the light microscope. Microscopes allow real-time recording of spontaneous or evoked spatio-temporal dynamics, data that can be used to develop models for how complex systems function. Today, cutting-edge microscopes can image below the diffraction limit of light (super-resolution microscopy), or over days, gently enough to allow an organism to develop and walk away (light-sheet microscopy). Yet, microscopy studies of biological systems largely rely on human control or pre-defined acquisition parameters, to identify features of interest, perturb the system, and collect data in a given location and at a given timescale. This is because subtle changes in protein dynamics and assembly patterns often herald events of interest – _too subtle and unreliable to act as inputs to existing microscope automation.

Advances in intelligent systems and adaptive control have the potential to revolutionize how microscopy data is collected, and to then enable breakthroughs in our understanding of biological systems. We propose to develop a neural network-based microscope controller that is capable of detecting image signatures related to biological activity, and in response, adapting illumination patterns at multiple locations across an imaging field of view. The proposed project aims to build upon a neural-network microscope control framework previously developed in the Manley group, to make it suitable for spatially and temporally adapted control. We will apply this to push beyond the state-of-the art, using as proof-of-concept organismal studies performed in the Oates group, and biofilm studies in the Manley group.

Video-based action segmentation by learning world models from language

Many questions in biology, from development to neuroscience and medicine require the identification of finegrained behaviors. We will develop novel computer vision and natural language processing technology to improve behavioral analysis in biology and medicine. Specifically, we will build deep learning models that can efficiently learn joint representations from video and heterogeneous data sources (e.g., textual descriptions, knowledge graphs). To do so, we will mine the written literature as well as video sharing platforms to extract a knowledge graph of behavior and then learn tri-modal models based on vision, language and this knowledge graph. We believe that these models will be able to more robustly and efficiently generalize to various applications in biology.

Learned Scalable high Dynamic Range imaging in radio astronomy

Next-generation radio telescopes such as the Square Kilometer Array (SKA) will observe the sky with unprecedented resolution, sensitivity, and survey speed. However, this precise instrument will demand reliable, precise, and high dynamic range deconvolution techniques to form images. The popular CLEAN algorithm, while efficient, often produces images of suboptimal quality. In recent years convex and nonconvex optimization algorithms have been demonstrated to produce images with superior quality, but at the cost of efficiency and scalability. Deep learning solutions offer a compromise between speed and quality, but at the cost of reliability and generalizability. In this project, the researchers will leverage the expertise between the astronomy and signal processing research groups at EPFL to develop an end-to-end imaging solution that is precise, robust, and scalable.

Fast multifunctional microscopy for photoelectrochemistry and bioimaging

Scanning probe methods – and in particular, the combination of scanning ion conductance microscopy (SICM) and scanning electrochemical microscopy (SECM) – have emerged as unique tools for studying materials and mechanisms in complex, multistep chemical reactions such as CO2 reduction. However, they are notoriously slow in image acquisition, making them ill-suited for studying the dynamics of energy conversion processes. In this project, these two EPFL labs will develop advanced hardware and software components for a unique, fast SICM-SECM imaging method that can be easily deployed within the EPFL community, and beyond. Their method has great potential for the design of energy devices, as well as emerging cross-disciplinary applications such as the nanoelectrochemistry of single-cell signaling.

A more effective 3D-imaging system for Earth System Science and Navigation

3D image reconstruction or depth estimation is at the core of applications in navigation as well as Earth system science. Significant advances have been made in the field of computer vision to obtain 3D information from various types of cameras. Yet, these techniques still face limitations for a number of applications. In this project, the VITA (Prof. Alahi ) and EERL (Prof. Schmale) groups will pool their complementary skills to develop new machine-learning-based methods that can estimate depth from a large number of camera configurations, including from a novel 360° camera application. With their work, the teams will spearhead developments in the domain of 3D wave reconstruction and sea-ice classification, as well as autonomous navigation on water. iThe learning framework will be available as an open-source library that caters to the needs of many imaging applications.

3D nanoscopic imaging of genome ultrastructure in cells

Each human cell contains around two meters of DNA tightly packaged in its nucleus. An exquisite organization is critical to ensure that the DNA can be accessed by the many important genetic processes. This organization is achieved by wrapping the DNA around millions of tiny protein spindles, forming a complex called chromatin. Chromatin governs many key cellular functions and, when malfunctions in its organization can lead to serious diseases. As of now, there exist no imaging methods that allows scientists to observe chromatin organization directly in the nucleus without seriously interfering with its local structure. In this project, two EPFL labs from different schools will develop novel methods for imaging the ultrastructure of chromatin on the level of individual genes and their regulatory regions in cells using in situ fluorescent chemical labeling, 3D nanoscopy and sequencing-based methods.

High-speed multimodal super-resolution microscopy

In this project, scientists from two EPFL labs will combine their know-how to develop a new high-speed microscopy system that can reveal single-molecule dynamics with unprecedented detail, including in liquids. The system will also allow scientists to assess how individual molecules behave, interact and self-organize at the solid-liquid interface. More specifically, they will enhance the system’s high-speed temporal acquisition capability by incorporating the single-photon avalanche diode (SPAD) arrays developed in Prof. Charbon’s lab into the state-of-the-art widefield super-resolution microscope developed in Prof. Radenovic’s lab.

Connecting imaging to mechanical measurements

When it comes to characterizing mechanics at the cellular scale, the accuracy and precision of current methods are still limited. In this project, Prof. Kolinski and Prof. Persat will connect imaging to mechanical measurements by developing a set of hardware and software tools that can measure microscale 3D force fields and surface stresses. The goal will be to improve our understanding of the function of forces in the physiology of biological systems. The new tools can then be used to study the mechanobiology of bacterial pathogens and will be widely applicable in other fields as well, such as microscale mechanics and the study of soft matter.

Towards more accurate large-scale gene expression profiles

Spatial transcriptomics – a nascent field arising from the combination of cutting-edge microscopy with gene-specific in-situ labeling – can be used to generate large gene expression profiles of messenger RNA. This gives scientists an indication of the relative expression rates of different genes in the same environment. EPFL scientists at these two labs are are profiling the expression of up to two hundred genes simultaneously in the developing brain using Hybridization In Situ Sequencing (HybISS). This cutting-edge method relies on computational processing methods that are still immature, can be hard to use and error-prone.

In this project, Dr. La Manno and Dr. Weigert will develop a computational spatial transcriptomics framework – called Codebook-Aware ILP Detection and Tracking (CBAIDT) – that leverages modern computer vision techniques while using a novel tracking approach to substantially increase the accuracy and robustness of gene expression map generation.

Imaging Labs 2

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Adaptive Systems Laboratory

Prof. Ali Sayed

ML tools ML theory distributed processing decentralized learning functional connectivity hyperspectral images

Laboratory for Biomolecular Modeling

Prof. Matteo Dal Peraro

cryo-EM integrative structural biology molecular modelling

smart microscopy molecule tracking proteins dynamics PAINT imaging super-resolution microscopy SMLM

super-resolution microscopy open hardware nanoscale biology high-speed imaging nanoprobes MEMS AFM STM

MRI in-vivo neurochemistry multinuclear spin physics metabolic imaging MR spectroscopy

Laboratory of Digital Epidemiology

Prof. Marcel Salathé

ML tools network epidemiology food recognition contact tracing

Laboratory of Soil Mechanics

Prof. Lyesse Laloui

3D X-Ray tomography micro-CT scans crack detection soil mechanics

segmentation nanomaterials tracking of bubbles TEM

Brisken Lab

Prof. Cathrin Brisken

fluorescence microscopy breast cancer digital pathology

Laboratory for Experimental Museology

Prof. Sarah Kenderdine

virtual reality digital humanities interactive scientific visualization media naviguation

Laboratory of Innate Immunity

Prof. Andrea Ablasser

cryo-EM immune response DNA sensing confocal imaging

De Palma Lab

Prof. Michele de Palma

tumor environment micro-CT images tumor segmentation

ML tools 3D brain imaging systems biology yeast segmentation

ML tools ML theory information theory

Digital Humanities Chair

Prof. Frederic Kaplan

ML tools digital humanities semantic segmentation historical images content mining document segmentation

neurodegenerative diseases smart microscopy motor neuroscience automated lightsheet microscopy functional imaging infrared imaging CLEM

ML tools semantic segmentation remote sensing pattern extraction image interpretability wildlife conservation urban studies


Swiss Plasma Center

Prof. Ambrogio Fasoli

plasma physics tomographic inversion thermonuclear fusion

optical biosensors biomarkers cancer diagnostics low-cost sensing devices single-wavelength imaging nanophotonics

Neuroengineering Laboratory

Prof. Pavan Ramdya

image analysis drosophilia locomotion 2-photon microscopy neural activity imaging computational models limb tracking ML tools

Signal Processing Laboratory 4

Prof. Pascal Frossard

ML tools ML theory image analysis digital pathology robustness of neural networks graph signal processing

ML tools remote sensing precipitation dynamics 3D snowflake structure

label-free imaging protein aggregation neurodegenerative diseases cryo-EM CLEM

Laboratory for Topology and Neuroscience

Prof. Kathryn Hess Bellwald

applied algebraic topology neuronal morphologies

Lemaître Lab

Prof. Bruno Lemaître

Plant Ecology Research Laboratory

Prof. Charlotte Grossiord

plant ecology 3D X-Ray tomography micro-CT scans segmentation of plant roots

energy management thermal infrared imaging temperature extraction

Laboratory of Psychophysics

Prof. Michael Herzog

electrical neuroimaging MEG EEG visual information processing

Laboratory of Optics

Prof. Demetri Psaltis

label-free imaging ML tools holography optical diffraction tomography cochlear imaging multimode optical fibers

cryo-EM time-resolved EM ultrafast TEM revitrification electron pulses

Engineering Mechanics of Soft lnterfaces

Prof. John Martin Kolinski

high-speed imaging particle tracking soft matter interface fluid mechanics interferometry virtual frame technique FTIR

3D X-Ray tomography seismic behaviour structure dynamics crack detection stereo vision DIC

Neural Microcircuitry Laboratory

Prof. Henry Markram

neuroimaging single cell reconstruction voltage-sensitive dyes imaging

Laboratory of Molecular Microbiology

Prof. Melanie Blokesch

fluorescence microscopy bacterial regulatory networks phase contrast imaging

genome organization systems biology spinning disk confocal microscopy

Medical Image Processing Lab

Prof. Dimitri Van de Ville

fMRI ML tools image processing mathematical imaging

Weigert Group

Prof. Martin Weigert

ML tools image analysis digital pathology nuclei segmentation

Signal Processing Laboratory 2

Prof. Pierre Vandergheynst

Hummel Lab

Prof. Friedhelm Hummel

fMRI EEG cognitive rehabilitation DTI transcranial magnetic stimulation

TCV – Tokamac Physics

Prof. Christian Theiler

plasma physics tomographic inversion thermonuclear fusion

Laboratory of Sensory Processing

Prof. Carl Petersen

2-photon microscopy neural activity imaging synaptic activity calcium imaging

neurodegenerative diseases sample preparation cryo-EM data collection

Composite Construction Laboratory

Prof. Anastasios Vassilopoulos

advanced composite materials fractography analysis DIC


Persat Lab

Prof. Alexandre Persat

confocal imaging mechanomicrobiology interferometric scattering

Suter Lab

Prof. David Suter

fluorescence microscopy cellular biology gene regulation cell identity

Laboratory of Nanoscale Biology

Prof. Aleksandra Radenovic

label-free imaging smart microscopy super-resolution microscopy mesoscopic imaging optical projection tomography quantitative phase imaging nanofluidics AFM EM SMLM

fluorescence microscopy bacterial mechanics

Laboratory of Semiconductor Materials

Prof. Anna Fontcuberta

semiconductor materials nanowires cathodoluminescence imaging SEM TEM

MRI fMRI virtual reality multisensory processing body perception

Gaznat Chair on GeoEnergy

Prof. Brice Lecampion

acoustic imaging geomaterials fracture propagation inverse problems

ML theory mathematical imaging spatial point patterns earth observation

Advanced Quantum Architecture Lab

Prof. Edoardo Charbon

high-speed optical sensing processing architectures cryogenic electronics LiDAR SPAD

Computer Vision Lab

Prof. Pascal Fua

ML tools ML theory image analysis detection in cryo-EM human tracking features extraction

developmental biology smart microscopy open hardware light-sheet microscopy oscillatory dynamics

TEM cryo-Lorentz microscopy femtosecond laser pulses ultrafast imaging

Realistic Graphics Lab

Prof. Wenzel Jakob

image processing mathematical imaging source localization interferometric imaging acoustic imaging

Integrated Circuits Laboratory

Prof. Christian Enz

CMOS image sensors integrated circuits

label-free imaging nonlinear light scattering water characterization 2-photon imaging

3D X-Ray tomography micro-CT scans solid mechanics flexible structures

Biomedical Imaging Group

Prof. Michael Unser

MRI ML tools ML theory super-resolution microscopy image processing cryo-EM optical imaging image reconstruction mathematical imaging

Max Planck-EPFL Nanolab

Prof. Magalí Lingenfelder

ML tools photonics devices optical phase imaging retinal imaging multimode optical fiber imaging digital optics

Gönczy Lab

Prof. Pierre Gönczy

developmental biology expansion microscopy fluoresence microscopy

magnonics nanomagnetism cryo-Lorentz microscopy

ML tools intelligent transport depth estimation autonomous navigation motion forecasting

Computational Neuroscience and AI

Prof. Alexander Mathis

ML tools ML theory image analysis animal tracking object recognition pose estimation

Signal Processing Laboratory 5

Prof. Jean-Philippe Thiran

ML tools ML theory image analysis image reconstruction ultrasound imaging diffusion MRI

Computational Solid Mechanics Laboratory

Prof. Jean-Francois Molinari

numerical modelling friction and fracture ultrahigh-speed photography optical profilometer SEM DIC

Unsteady Flow Diagnostics Laboratory

Prof. Karen Mulleners

vortex dynamics particle tracking unsteady flows time-resolved PIV

ML tools ML theory image analysis animal tracking object recognition pose estimation system neuroscience smart microscopy adaptative behaviour

Computational X-ray Imaging Laboratory

Prof. Manuel Guizar Sicairos

coherent lensless imaging phase retrieval ptychography tomography X-ray imaging

neural implants retinal prosthesis artificial vision

Geodetic Engineering Laboratory

Prof. Bertrand Merminod

image reconstruction remote sensing depth estimation UAV drone mapping position determination

ML tools remote sensing LiDAR spatial analysis geovisualization pattern analysis

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