Three-dimensional segmentation and symbolic representation of cerebral vessels on 3DRA images of arteriovenous malformations.

BACKGROUND: Endovascular embolization is a minimally invasive interventional method for the treatment of neurovascular pathologies such as aneurysms, arterial stenosis or arteriovenous malformations (AVMs). In this context, neuroradiologists need efficient tools for interventional planning and microcatheter embolization procedures optimization. Thus, the development of helpful methods is necessary to solve this challenging issue. METHODS: A complete pipeline aiming to assist neuroradiologists in the visualization, interpretation and exploitation of three-dimensional rotational angiographic (3DRA) images for interventions planning in case of AVM is proposed. The developed method consists of two steps. First, an automated 3D region-based segmentation of the cerebral vessels which feed and drain the AVM is performed. From this, a graph-like tree representation of these connected vessels is then built. This symbolic representation provides a vascular network modelization with hierarchical and geometrical features that helps in the understanding of the complex angioarchitecture of the AVM. RESULTS: The developed workflow achieves the segmentation of the vessels and of the malformation. It improves the 3D visualization of this complex network and highlights its three main components that are the arteries, the veins and the nidus. The symbolic representation then brings a better comprehension of the vessels angioarchitecture. It provides decomposition into topologically related vessels, offering the possibility to reduce the complexity due to the malformed vessels and also determine the optimal paths for AVM embolization during interventions planning. CONCLUSIONS: A relevant vascular network modelization has been developed that constitutes a breakthrough in the assistance of neuroradiologists for AVM endovascular embolization planning.

Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology.

In the last 20 years, 3D angiographic imaging has proven its usefulness in the context of various clinical applications. However, angiographic images are generally difficult to analyse due to their size and the complexity of the data that they represent, as well as the fact that useful information is easily corrupted by noise and artifacts. Therefore, there is an ongoing necessity to provide tools facilitating their visualisation and analysis, while vessel segmentation from such images remains a challenging task. This article presents new vessel segmentation and filtering techniques, relying on recent advances in mathematical morphology. In particular, methodological results related to spatially variant mathematical morphology and connected filtering are stated, and included in an angiographic data processing framework. These filtering and segmentation methods are evaluated on real and synthetic 3D angiographic data.

Continuous maximum flow segmentation method for nanoparticle interaction analysis.

In recent years, tomographic three-dimensional reconstruction approaches using electrons rather than X-rays have become popular. Such images produced with a transmission electron microscope make it possible to image nanometre-scale materials in three-dimensional. However, they are also noisy, limited in contrast and most often have a very poor resolution along the axis of the electron beam. The analysis of images stemming from such modalities, whether fully or semiautomated, is therefore more complicated. In particular, segmentation of objects is difficult. In this paper, we propose to use the continuous maximum flow segmentation method based on a globally optimal minimal surface model. The use of this fully automated segmentation and filtering procedure is illustrated on two different nanoparticle samples and provide comparisons with other classical segmentation methods. The main objectives are the measurement of the attraction rate of polystyrene beads to silica nanoparticle (for the first sample) and interaction of silica nanoparticles with large unilamellar liposomes (for the second sample). We also illustrate how precise measurements such as contact angles can be performed.