VAILLANT Ghislain

Research Software Engineer

Université Paris Dauphine-PSL

ghislain.vaillant [at] icm-institute.org

Short bio

PhD, King’s College London

Research project

Clinica

Short abstract

Clinica is the software platform for clinical neuroimaging studies involving processing of multimodal data (imaging and phenotypic) for patients with cognitive diseases. My work consists in extending this platform to provide its functionalities as a service in the Cloud in order to serve a wider scientific audience.

Thibeau-Sutre Elina

PhD Student

Paris Brain Institute

elina.ts [at] free.fr

Short bio

Master degree (Diplôme d’ingénieur) at Ecole des Mines de Paris

Master degree in bio medical engineering (ESPCI, Université Paris Descartes, Arts et Métiers)

Thesis title

Unsupervised learning from neuroimaging data to identify disease subtypes in Alzheimer’s disease and related disorders.

Short abstract

The objective of my PhD thesis is to develop and evaluate clinically-relevant approaches for unsupervised learning to characterize disease heterogeneity in AD and related dementias. Specific objectives include: 1) To adequately account for normal variability. For instance, in a clustering approach, the aim would be to cluster the deviations from normal variability, rather than the raw characteristics of the patients. 2) To design approaches that can handle the structure and high-dimensionality of data of neuroimaging data. 3) To define clinically-relevant measures to assess the results of the unsupervised learning.

GODARD Charlotte

PhD Student

Institut Pasteur

charlotte.godard [at] pasteur.fr

Short bio

Engineer degree – TELECOM PHYSIQUE STRASBOURG

Master degree in Imaging, Robotics and Engineering for Healthcare – UNIVERSITY OF STRASBOURG

Thesis title

Semi-automatic and amortized developments of transfer function for surgery planning in virtual reality.

Short abstract

Interpretation of medical images, such as MRI or CT-scan, can be challenging for a non-radiologist expert because of the various image quality and of the similarities between different structures of interest. However, surgeons need to understand these images to prepare surgeries and define corresponding anatomical landmarks. As universal segmentation is not possible due to the diversity of images between patients, we focused on the optimization of the visualization process applied only on the raw data. The AVATAR MEDICAL platform uses virtual reality for an intuitive visualization and manipulation of the images. Visualization parameters (color, transparency) are currently defined manually using an user-friendly transfer function desktop interface. The objective of the thesis is to automate the transfer function generation for a faster isolation of the structures of interest in the image, by combining a statistical approach and pre-trained models.

COUVY-DUCHESNE Baptiste

Postdoctoral Researcher

ICM Institute

baptiste.couvy [at] icm-institute.org

Short bio

PhD, Queensland Brain Institute, the University of Queensland
MSc, ENSAI & University of Rennes 1

Research project

I work on developing or adapting methods for the analysis of big-data neuroimaging (e.g. UK Biobank). Analyses include, association, brain mapping and brain based prediction.

Short abstract

The growing availability of large MRI imaging samples (e.g. UK Biobank, Human Connectome Project, Alzheimer’s Disease Neuroimaging Initiative) provides a great opportunity to deepen our understanding of the relationship between brain MRI measurements and psychiatric or neurological disorders, as well as socio-economic variables, cognition or personality.  My research focusses on adapting some of the methods used in genetics that have I learned during my PhD in order to quantify the information contained in each type of image modality, identify brain markers of psychiatric (affective disorders, substance use) and neurological disorders (Alzheimer’s, Parkinson’s) and build brain scores of these disorders that may be help to assist diagnosis or evaluate response to treatment.

FOURNIER Laure

Medical imaging

laure.fournier [at] parisdescartes.fr

Laure Fournier

Short bio

MD, PhD, Professor of Radiology, Hôpital Européen Georges Pompidou, Université de Paris. Ecole Normale Supérieure (1991-1995), Research Fellow, UCSF, San Francisco, CA, USA (2001-2003). Responsible for the organisation of courses in Artificial Intelligence in Radiology for radiology residents, Member of the working group on Artificial Intelligence for the CERF – SFR, Member of the Scientific Committee of the DRIM France IA database. Grants over 650 k€ on radiomics and AI in medical imaging projects.

Topics of interest

Medical imaging, machine learning, radiomics, computer vision

Project in Prairie

Our project will focus on three approaches: 1) methodological developments on radiomics, i.e. high throughput extraction and selection of features from medical images using strategies including feature engineering, and deep learning and neural networks; 2) constitution of real-time prospective databases to obtain exploitable training and test data for the applications developed in Prairie; 3) integration of multimodality and multiparametric data stemming from multi-scale imaging going from microscopy to anatomical (radiology) and functional imaging.

Quote

Developments in computer vision need to translate into benefits for patients by transferring tools and applications developed for non-medical images to microscopic and macrsocopic medical imaging. The integration of this very diverse data to obtain a comprehensive view of a patient and his disease is a challenge which we must undertake in Prairie. The relative low numbers of patient data compared to the large number of features and parameters describing the patient and his disease, and the time-consuming annotation, remain important challenges and will require new tools which can train and learn on datasets with a weaker level of human supervision.

DURRLEMAN Stanley

Statistical learning, imaging, neurosciences

stanley.durrleman [at] inria.fr / Twitter: @SDurrleman

Stanley Durrleman

Short bio

Stanley Durrleman is heading the joint Inria / ICM ARAMIS research team at the Brain Institute (ICM) in Paris. He is the founding director of the ICM centre for neuroinformatics. His research has earned him several international awards, including a European Research Council (ERC) award in 2015.

Topics of interest

Geometry and learning, neuroimaging, brain disorders, disease modeling, digital twins

Project in Prairie

We will develop novel statistical and computational approaches at the cross-roads of geometry and learning. These approaches built on generic principles will allow the exploitation of a large variety of structured and unstructured data such as clinical data, structural and functional imaging. These methods are well suited to deal with repeated data from the same patients over time (i.e. longitudinal data), so that they can be used to synthetize digital models of disease progression. The personalization of such models to new patient data will enable the implementation and evaluation of personalized therapeutic strategies.

Quote

Better understanding the brain and its disorders is probably the most fascinating scientific and medical challenge of this century. The repeated failures to find efficient treatments against most neurological diseases require to explore radically different approaches. At the core of one of the major European hospital and neuroscience research institute, we develop novel data-driven approaches to exploit large databases of neuroscience data including imaging, clinical, physiological and genomics data. We simulate the progression of neurodegenerative diseases. We design and evaluate decision support systems informed by personalized prediction of disease progression. Our research contributes therefore to the emergence of a precision medicine in neurology.

COLLIOT Olivier

Machine learning for medical imaging

olivier.colliot [at] upmc.fr

Olivier Colliot

Short bio

Olivier Colliot is Research Director at CNRS and the founding head of the ARAMIS Lab, a joint team between CNRS, Inria, Inserm and Sorbonne University at the Brain and Spine Institute (ICM). Founded in 2012, ARAMIS gathers about 35 people dedicated to data science and AI for studying diseases of the brain. Prior to that, Olivier Colliot obtained a PhD in Computer Science from Telecom ParisTech in 2003, was a postdoctoral fellow at McGill University from 2003 to 2005 and joined the CNRS as a permanent researcher in 2006. He is the author of over 80 journal papers and is a member of the editorial board of Medical Image Analysis, one of the two leading journals of the field.

Topics of interest

Machine learning, computer vision, medical imaging, multimodal medical data (imaging, genomics)

Project in Prairie

His research will be dedicated to interpretable machine learning for neuroradiology. The main research threads are: i) the design of approaches for more interpretable AI, ii) generic computer-aided diagnosis systems, iii) integration of multimodal data and iv) methodologies for reproducible research. Medical applications will be devoted to neurological diseases, in particular using large-scale clinical routine data.

Quote

Brain imaging is a domain in which AI hold major promises. However, current systems are not interpretable and too narrow. These are major barriers to their adoption by clinicians. I firmly believe that fundamental research advances are needed to make systems more interpretable and generic. I hope that these ultimately lead to better diagnosis and care of patients. I am really excited to conduct this project within PRAIRIE, which will open new fruitful collaborations with academic and industrial partners. I also believe we have a major role to play in training in AI the next generation of engineers but also clinicians, that our country needs.

BURGOS Ninon

Medical Image Computing

ninon.burgos [at] icm-institute.org

Ninon Burgos

Short bio

Ninon Burgos is a CNRS researcher at the Paris Brain Institute, in the ARAMIS Lab. She completed her PhD at University College London, UK. In 2019 she received the ERCIM Cor Baayen Young Researcher Award, which is awarded each year to a promising young researcher in computer science or applied mathematics. Her research currently focuses on the development of computational imaging tools to improve the understanding and diagnosis of neurological diseases.

Topics of interest

Medical imaging, computer-aided diagnosis, machine learning

Project in Prairie

Ninon Burgos will focus on the individual analysis of medical images to improve differential diagnosis and strengthen personalised medicine. This project will involve developing advanced computational representations of multimodal imaging data and building flexible decision support systems. The framework will be applied to brain images to assist in the diagnosis of neurological diseases.

Quote

Neuroimaging offers an unmatched description of the brain, which explains
its crucial role in the understanding and diagnosis of neurological disorders. There is a critical need to develop new methods that can improve the characterisation of each patient’s pathology, and to build decision support systems more sensitive and easier to interpret.