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Robust vascular network extraction

and understanding within hepatic biomedical images
Consult publications

Our research project

Cardiovascular diseases and other blood vessels disorders increase in the world-wide scale and in particular in Occident. The evolution of computer science in researches investigating vascular networks has raised the interest in numerical reconstructing and understanding of those complex tree-like structures. The R-VESSEL-X project proposes original and robust developments of image analysis and machine learning algorithms integrating strong mathematical frameworks, e.g. digital geometry and topology, mathematical morphology, or graphs for reconstructing vessels of the liver beyong medical image content. Another objective of R-VESSEL-X is to diffuse research works in an open-source way, with the developments of plug-ins compatible with the ITK and VTK librairies largely popularized by the KITWARE company. This project will also include benchmarks composed of images, associated ground-truth and quality metrics, so that researchers and engineers evaluate their novel contributions. The consortium of R-VESSEL-X is composed of the following laboratories: Institut Pascal (coordinator, Le Puy-en-Velay/Clermont-Ferrand), LIRIS (Lyon), CReSTIC (Reims), working together with the KITWARE company (Lyon). This is a highly pluridisciplinary group composed of researchers in computer science-related topics (biomedical image processing, numerical simulation and analysis), applied mathematics (digital geometry and topology, mathematical morphology), working with medical doctors (radiologists, hepatologists) and young researchers and developers enrolled for the project.

Kick-off meeting (photos)

First meeting has been organized on 10-11 January 2019

Launching R-Vessel-X (video)

This video presents the first meeting of the project

Meeting 2020 (photos)

Second meeting, on 9-10 January 2020

Consortium

R-Vessel-X shares open-source codes

Publications

 

Vesselness filters: A survey with benchmarks applied to liver imaging

Abstract:  The accurate knowledge of vascular network geometry is crucial for many clinical applications such as cardiovascular disease diagnosis and surgery planning. Vessel enhancement algorithms are often a key step to improve the robustness of vessel segmentation....

Component-graph construction

Abstract: Component-trees are classical tree structures for grey-level image modelling. Component graphs are defined as a generalization of component trees to images taking their values in any (totally or partially) ordered sets. Similarly to component-trees,...