Internship For Automated Quality Assessment Of Brain Mri Data H/F

INRIA

Internship for automated quality assessment of brain MRI data
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Niveau de diplôme exigé : Bac +4 ou équivalent

Fonction : Stagiaire de la recherche

A propos du centre ou de la direction fonctionnelle

Inria is the French National Institute for Research in Digital Science, of which the Inria Côte d'Azur University Center is a part. With strong expertise in computer science and applied mathematics, the research projects of the Inria Côte d'Azur University Center cover all aspects of digital science and technology and generate innovation. Based mainly in Sophia Antipolis, but also in Nice and Montpellier, it brings together 47 research teams and nine support services. It is active in the fields of artificial intelligence, data science, IT system security, robotics, network engineering, natural risk prevention, ecological transition, digital biology, computational neuroscience, health data, and more. The Inria Center at Université Côte d'Azur is a major player in terms of scientific excellence, thanks to the results it has achieved and its collaborations at both European and international level.

Contexte et atouts du poste

This project aims at developing an AI algorithm for the automated quality control of brain MRI scans. It will be conducted under the supervision of:

- Benjamin Billot is a researcher in the Epione team at Inria Université Côte d'Azur.
- Ninon Burgos is a Directeur de recherche in the Aramis Lab in Paris, a joint team between CNRS and Inria.

You are free to choose your main work location, with possibilities of funded travels between the two. In both cases, the Epione team and the Aramis lab have a strong multidisciplinary composition, bringing together researchers in machine learning and medical doctors. This project will give you the opportunity to interact with the PhD students and engineers of the lab, as well as our medical collaborators at the Pitié-Salpêtrière hospital.

Overall, this project combines a strong image generation component with state-of-the-art deep learning architectures (diffusion models, CNNs, etc.). If successful, the methodological developments will be integrated into the open-source software ClinicaDL designed to enable reproducible neuroimaging processing with deep learning.

Mission confiée

The AP-HP (Assistance Publique-Hôpitaux de Paris) brings together medical imaging data from all hospitals in the Paris region. This resource provides a fantastic opportunity to train and test effective machine learning models for neuroimaging tasks.

However, unlike curated datasets, the nature and quality of AP-HP images is very heterogeneous. Indeed, many MRIs are unusable because of poor image quality due to artefacts (noise, motion, poor tissue contrast...). Moreover, their meta-data is often inaccurate, which means that automatic data retrieval based on some criteria (sequence, resolution, pathology) is not trustable. Hence, it is crucial to detect all these effects directly from the images. Yet, visual checking by human raters is impossible due to the large volume of images. Therefore, there is a need for automatic tools that can reliably perform data quality control (QC) on CDW images and extract information from them.

Principales activités

This project consists of developing a new tool for automatic quality control and image-based information retrieval from MRIs scans. We propose here to rely on simulation strategies to alleviate the need for manual ground truths for the presence/strengths of artefacts. Moreover, instead of adapting our QC deep learning network to multiple MRI types (i.e., domains), we propose here to adopt a domain randomisation approach based on SynthSeg. In this paradigm, parametric generative models are used to synthesise extremely variable images of randomised aspect that are then used to train a domain-agnostic AI system.

A review of the literature will be necessary to identify (a) state-of-the-art domain randomisation techniques based on deep learning tools; (b) the best ways of generating synthetic datasets containing varied artefacts. Once identified, the techniques will be implemented and tested on research data before being applied to images from the AP-HP. Finally, experiments will be carried out to determine whether this method leads to a better estimate of the overall image quality than our current approach.

Compétences

- Degree/studies in computer science, image analysis and/or applied mathematics.
- Strong interest for medical applications.
- Knowledge of deep learning.
- Knowledge in digital image processing and medical imaging.
- Good programming skills in Python.
- Good writing skills.
- Good relational and communication skills to interact with professionals from various backgrounds.

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage

Rémunération

Traineeship grant depending on attendance hours