Corcos, Ludovic

Deslauriers-Gauthier, Samuel

Papadopoulo, Théodore

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HAL Id: hal-03879971https://hal.inria.fr/hal-03879971Submitted on 30 Nov 2022HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Forward modelling of M/EEG: Towards a newautomatic head and brain tissue segmentation systemLudovic Corcos, Théodore Papadopoulo, Samuel Deslauriers-GauthierTo cite this version:Ludovic Corcos, Théodore Papadopoulo, Samuel Deslauriers-Gauthier. Forward modelling of M/EEG:Towards a new automatic head and brain tissue segmentation system. SophI.A 2022 : fifth interna-tional AI conference, Nov 2022, Sophia Antipolis, France. Proceedings of SophIA 2022. ￿hal-03879971￿FORWARD MODELLING OF M/EEG: TOWARDS A NEW AUTOMATIC HEAD AND BRAIN TISSUE SEGMENTATION SYSTEMLudovic Corcos 1,2(ludovic.corcos@gmail.com), Theodore Papadopoulo 1(theodore.papadopoulo@inria.fr) and Samuel Deslauriers-Gauthier 1(samuel.deslauriers-gauthier@inria.fr)1 Centre INRIA d'Université Côte d'Azur - 2 ISIMA, Université Clermont AuvergneThis work has been supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.AbstrActMagnetoencephalography and electroencephalography  (together M/EEG) are imaging modalities that allow the non-invasive measurement of the magnetic field and the electric potential generated by cortical activity. Inferring which brain areas generated the observed M/EEG measurements is not a trivial task and is referred to as the inverse problem. A common way to solve the problem is to assume than brain sources act like current dipoles in a volume conductor, in this case the head whose geometry can be obtained from magnetic resonance imaging (MRI). The relationship between brain sources and M/EEG measurements can therefore be modeled, a process called the solving forward problem. This process can be seen as injecting anatomical priors into the inverse problem [1]. However, extracting the anatomical information from MRI needed to solve the forward problem is lengthy and tedious with existing tools. In this work, we present the first step in the creation of an automated pipeline to generate a volume conductor model from T1 and T2 images.Objectives ● Obtain a 7-tissue segmentation of the MRI of each individual, consisting of skin, bone, muscle, white matter, gray matter, cerebrospinal fluid and background. → Note that existing segmentation systems do not consider the skin, bone, and muscle layers, instead focus only on the brain [2]. ● Given this segmentation, a 3D model can be built where a specific electrical conductivity is assigned to each label.dAtAsetImage enhancementBrightness, contrast, smoothing, background removal, etc...Non-linear registrationObtaining the deformation field of each subjectAutomatic segmentationUsing FSL FAST - FMRIB’s Automated Segmentation Tool [3]Manual segmentationSkin, bone, muscle, and cerebrospinal fluidReverse mapping and dataset creationFor each T1/T2, we associate a segmentation where we applied an inverse registration thanks to the deformation field generated previouslyMRIs from HCP**HCP - Human Connectome Project : project to construct a map of the complete structural and functional neural connections in vivo within and across individuals. Train Test ValidationT1 780 222 111T2 780 222 111Mask 780 222 111Data distribution for the dataset creationtrAiningTo segment the MRI volumes, we trained our own deep learning architecture, which is an adaptation of the U-NET model, under Tensorflow. Our 3D U-NET architecture will process dense volumetric data whose methods are inspired by the work of Çiçek et al. [4]The cost of the error is modelized by the following loss function:To follow how the learning process is going we calculate some metrics:resultsSource MRI Resulting segmentationcOnclusiOnIn this work, we presented an automated system to segment tissues which will be assigned specific conduction values for the construction of the volume conductor. This is an important first step in the creation of an automated pipeline to solve the forward problem in M/EEG.references[1] Samuel Deslauriers-Gauthier, Jean-Marc Lina, Russell Butler, Kevin Whittingstall, Guillaume Gilbert, Pierre-Michel Bernier, Rachid Deriche, and Maxime Descoteaux. White matter information flow mapping from diffusion MRI and EEG. NeuroImage (2019).[2] Ivana Despotovi´c, Bart Goossens, and Wilfried Philips. MRI Segmentation of the Human Brain: Challenges, Methods, and Applications. Computational and mathematical methods in medicine (2015).[3] Yongyue Zhang, Michael Brady, and Stephen Smith. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imag (2001).[4] Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox and Olaf Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. arXiv (2016)Poster.indd   1 17/11/2022   11:12:34

Forward modelling of M/EEG: Towards a new automatic head and brain tissue segmentation system

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Forward modelling of M/EEG: Towards a new automatic head and brain tissue segmentation system

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