General Presentation

  • Introduction

METISLAB is an international research project (IRP) from the INSIS CNRS Institute established between CREATIS, Shanghai University, the Harbin Institute of Technology and Harbin Medical University. The IRP METISLAB brings together Chinese and French researchers to develop advanced methods for analyzing data from medical imaging, with a focus on cardiovascular imaging applications.

  • Missions and research topics

By capitalizing on the expertise and previous achievements of Chinese and French experts, METISLAB aims to co-develop new and advanced medical image analysis and modeling methods with applications mainly in cardiovascular imaging. Four research themes are organized in two axes. The first axis is more fundamental and studies theoretical aspects of image analysis, in particular advanced and generic processing methods to handle images of different types and complexity (scalar, vector and tensor), reconstruction of anatomical shapes and structures, and statistical and machine learning methods dedicated to data from medical imaging for analysis and diagnosis. The second axis is dedicated to applications in cardiovascular imaging. The 3D ultrastructure of organs, especially the myocardium, is explored at different scales and numerical models are developed. In addition, the way water diffuses into the tissue ultrastructure will be simulated in order to study its impact on magnetic resonance diffusion imaging. The relationship of ultrastructure to cardiac function is also investigated. Inverse modeling approaches are developed to identify individualized mechanical properties of the myocardium and vessels. Combining these parameters with other image-based anatomical and tissue measurements in multiparametric statistical approaches applied to large collections of clinical data should provide new ways to detect and characterize cardiovascular disease.

  • Research projects

- Multi-type/component image processing: local non-stationarity detection, tensor data processing (diffusion), multi-parametric imaging,
- Integrative statistical data analysis and machine learning: machine learning and noise reduction, automatic and accurate parameter estimation,
- Analysis and modeling of myocardial ultrastructure: multi-scale modeling of cardiac and vascular tissues, ultrastructure-diffusion relationship,
- Quantification, modeling and analysis of cardiac and vascular functions: estimation of cardiac function index by machine learning, integration of data from imaging and patient-specific modeling for characterization and prediction of injured myocardium, estimation of vascular mechanical properties from in-vivo imaging and patient-specific modeling

  • METISLAB partners

    • CREATIS, Université de Lyon, France
    • Shanghai University (SHU), Shanghai, China
    • Harbin Institute of Technology (HIT), China
    • Harbin Medical University (HMU), China