Sébastien Tourbier

Sébastien Tourbier
Lausanne University Hospital | CHUV · Service de radiodiagnostic et radiologie interventionnelle

PhD in Life Sciences; M.Sc. in Communication Systems

About

46
Publications
5,869
Reads
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327
Citations
Introduction
M.Sc. in Communication Systems, with a specialization in Signal and Image Processing at the Swiss Federal Institute of Technology (EPFL) and PhD in Life Sciences at the University of Lausanne (UNIL) with strong interests in Medical Imaging Technologies.
Additional affiliations
March 2017 - present
Lausanne University Hospital
Position
  • PostDoc Position
January 2015 - July 2015
Boston Children's Hospital and Harvard Medical School
Position
  • Visiting Graduate Student and Research Associate
February 2011 - May 2012
Bracco Group
Position
  • Engineer
Education
October 2012 - October 2016
Centre d'Imagerie BioMedicale
Field of study
  • Electrical engineering
February 2009 - September 2011
École Polytechnique Fédérale de Lausanne
Field of study
  • Communication systems - Specialization in image and signal processing

Publications

Publications (46)
Article
Full-text available
Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) im...
Article
Full-text available
Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-auto...
Chapter
Full-text available
A commonly adopted approach to carry out detection tasks in medical imaging is to rely on an initial segmentation. However, this approach strongly depends on voxel-wise annotations which are repetitive and time-consuming to draw for medical experts. An interesting alternative to voxel-wise masks are so-called “weak” labels: these can either be coar...
Article
Full-text available
We describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n = 20) discriminated briefly presented...
Article
Connectome Mapper 3 (CMP3) is an open-source processing pipeline software, written in Python 3, for multi-scale multi-modal connectome mapping of the human brain. It provides researchers with a unique workflow, implemented in accordance with the Brain Imaging Data Structure (BIDS) App framework, that leverages a number of widely adopted software to...
Chapter
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and excessive movement of fetuses have limited its clinical applicability. Previous studies have focused primarily on the accurate estimation of the motion parameters employing...
Preprint
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and excessive movement of fetuses have limited its clinical applicability. Previous studies have focused primarily on the accurate estimation of the motion parameters employing...
Article
Full-text available
Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples, which are extremely costly to obtain. Here, we present a DL model for aneurysm detection that ove...
Preprint
Full-text available
The discovery that the structural connectome serves as a compact representation of brain activity allowed us to apply compressed sensing to M/EEG source reconstruction of brain electrical activity. We show that the introduction of this biological constraint significantly increases the signal-to-noise ratio and spatial conspicuity of the reconstruct...
Article
Full-text available
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of...
Preprint
We develop an open-source tool for the retrospective estimation of inter-volume head-motion and eddy-current distortions, typically found in diffusion MRI (dMRI) data acquired with echo-planar imaging schemes. The implementation is “open-since-inception” to ensure transparency. By leveraging the widely used DIPY package and a user-friendly interfac...
Article
Full-text available
The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections. Despite this intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited. Here, we propose a new adaptive filter for estimating dynamic...
Article
Full-text available
Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient...
Preprint
Full-text available
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and excessive movement of fetuses has limited clinical application since it causes substantial signal fluctuations which can systematically alter observed patterns of functional...
Chapter
Full-text available
Accurate characterization of in utero human brain maturation is critical as it involves complex interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool complementary to the ultrasound gold standard to monitor the development of the fetus, especially in the case of equ...
Conference Paper
Diffusion Magnetic Resonance Imaging (dMRI) has become widely used to study in vivo white matter tissue properties non-invasively. However, fetal dMRI is greatly limited in Signal-to-Noise ratio and spatial resolution. Due to the uncontrollable fetal motion, echo planar imaging acquisitions often result in highly degraded images, hence the ability...
Preprint
Full-text available
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of...
Article
Full-text available
The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI, and diffusion MR...
Chapter
Diffusion Magnetic Resonance Imaging (dMRI) has become widely used to study in vivo white matter tissue properties non-invasively. However, fetal dMRI is greatly limited in Signal-to-Noise ratio and spatial resolution. Due to the uncontrollable fetal motion, echo planar imaging acquisitions often result in highly degraded images, hence the ability...
Article
Full-text available
We present the comparison of two-dimensional (2D) fetal brain biometry on magnetic resonance (MR) images using orthogonal 2D T2-weighted sequences (T2WSs) vs. one 3D super-resolution (SR) reconstructed volume and evaluation of the level of confidence and concordance between an experienced pediatric radiologist (obs1) and a junior radiologist (obs2)...
Preprint
Full-text available
The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal appro...
Preprint
Full-text available
We describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n=20) discriminated briefly presented fa...
Preprint
Full-text available
Supervised segmentation algorithms yield state-of-the-art results for automated anomaly detection. However, these models require voxel-wise labels which are time-consuming to draw for medical experts. An interesting alternative to voxel-wise annotations is the use of weak labels: these can be coarse or oversized annotations that are less precise, b...
Preprint
Full-text available
The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity. Here, we explore the potential of exploiting this structure in order to improve the signal representation properties of brain activity and its dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI and diff...
Article
Full-text available
Objective To assess the value of caudal EEG electrodes over cheeks and neck for high-density electric source imaging (ESI) in presurgical epilepsy evaluation, and to identify the best time point during averaged interictal epileptic discharges (IEDs) for optimal ESI accuracy. Methods We retrospectively examined presurgical 257-channel EEG recording...
Preprint
Full-text available
A commonly adopted approach to carry out detection tasks in medical imaging is to rely on an initial segmentation. However, this approach strongly depends on voxel-wise annotations which are repetitive and time-consuming to draw for medical experts. An interesting alternative to voxel-wise masks are so-called "weak" labels: these can either be coar...
Chapter
Relaxometry studies in preterm and at-term newborns have provided insight into brain microstructure, thus opening new avenues for studying normal brain development and supporting diagnosis in equivocal neurological situations. However, such quantitative techniques require long acquisition times and therefore cannot be straightforwardly translated t...
Preprint
Full-text available
Relaxometry studies in preterm and at-term newborns have provided insight into brain microstructure, thus opening new avenues for studying normal brain development and supporting diagnosis in equivocal neurological situations. However, such quantitative techniques require long acquisition times and therefore cannot be straightforwardly translated t...
Article
Full-text available
Objective Epilepsy diagnosis can be difficult in the absence of interictal epileptic discharges (IED) on scalp EEG. We used high-density EEG to measure connectivity in large‐scale functional networks of patients with focal epilepsy (Temporal and Extratemporal Lobe Epilepsy, TLE and ETLE) and tested for network alterations during resting wakefulness...
Article
Full-text available
We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or “network harmonics”...
Preprint
Full-text available
We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing; in order to derive the Fourier modes of the brain structural connectivity graph, or "network harmonics...
Article
Full-text available
Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle...
Preprint
Full-text available
Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle...
Article
Full-text available
Objective: Epilepsy is a network disease with epileptic activity and cognitive impairment involving large-scale brain networks. A complex network is involved in the seizure and in the interictal epileptiform discharges (IEDs). Directed connectivity analysis, describing the information transfer between brain regions, and graph analysis are applied...
Article
Full-text available
In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity a...
Poster
Full-text available
MR Image segmentation and reconstruction have traditionally been regarded as separate processes. Recently, a novel joint reconstruction-segmentation framework[1] showed that incorporating a segmentation prior in the compressed-sensing reconstruction process of MR images provides a segmentation that degrades less with increasing undersampling compar...
Presentation
Full-text available
Bias field corruption is highly present in fetal MRI scans and even emphasized when imaging is performed at 3T. This could affect the performance of super-resolution reconstruction techniques resulting in undesired artifacts in the reconstructed high-resolution images. We present a new method for slice-by-slice intensity inhomogeneity correction, i...
Thesis
Full-text available
Fetal magnetic resonance imaging (MRI) has been increasingly used as a powerful complement imaging modality to ultrasound imaging (US) for the clinical evaluation of prenatal abnormalities. Specifically, clinical application of fetal MRI has been significantly improved in the nineties by hardware and software advances with the development of ultraf...
Conference Paper
Full-text available
Recent advances in fetal T2w MRI and image analysis techniques offered new opportunities to analyze in vivo cortical folding, also known as gyrification, a good indicator of early brain maturation as it is one of the most dramatic structural changes happening during gestation. However, few studies have quantified the folding of the cerebral cortex...
Conference Paper
Full-text available
In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address...
Conference Paper
Full-text available
Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy, Total Variation (TV)-based...
Conference Paper
Full-text available
In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is highly time-consuming and therefore not a realistic solution for large-scale studies. Only few works have addressed this automatic extraction problem. In this study, we assess the validity of Mul...

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Projects

Projects (2)
Project
Designed a complete pipeline for high-resolution fetal brain MRI with novel image processing methods that improves fetal brain MRI analysis in terms of effectiveness, efficiency and robustness while minimizing user interactions with the ultimate goal to be translated into clinical practice.