Symbolic description of vascular tree-like structures : application to the brain vascular tree

Cette thèse s’intéresse à la description symbolique d’arborescences vasculaires issues d’images 3D multimodales. Ce travail vise à fournir un cadre méthodologique global pour l’analyse de telles structures et plus particulièrement pour l’arbre vasculaire cérébral. Le domaine d’application clinique visé est la neurochirurgie, notamment pour la planification du geste du praticien. Notre principale contribution est le développement d’une méthode de squelettisation 3D, adaptée aux formes tubulaires et judicieuse pour la description symbolique. Nous proposons de baser la méthode de squelettisation sur la construction de l’arbre de des plus courts chemins de Dijkstra. Ainsi, nous extrayons la branche principale qui correspond à la branche la plus longue de l’arbre de Dijkstra, puis, nous détectons de manière itérative chaque branche annexe en conservant les branches de l’arbre de longueur supérieure à un seuil fixé. Puisque notre squelettisation se réalise de manière itérative, nous possédons les informations locales à chacune des branches. De cette manière, la description symbolique est facilitée et consiste alors en un partitionnement du squelette permettant la collecte de ces informations. Les algorithmes ont été implémentés sous la plateforme logicielle du laboratoire, ArtiMed, et évalués sur données simulées et cliniques. L’évaluation des méthodes de squelettisation et de description symbolique a fait l’objet de l’élaboration d’un plan d’expérience spécifique consistant en une comparaison des résultats sur une série de 18 rotations du volume initial.This thesis describes the methodology and the evaluation of a symbolic description method applied on vascular trees from multimodal 3D images. This work aims to supply a global methodological framework for the analysis of such structures and, more particularly, for the cerebral vascular tree. The clinical application field is neurosurgery and particularly neurosurgery planning. Our method is based on the application of the minimum cost-spanning tree using Dijkstra’s algorithm and seems well appropriate to tubular objects. We skeletonize the structure in two stages: first, we extract the main branch which corresponds to the longest branch of the Dijkstra’s tree, then, we detect iteratively every secondary branch by keeping the branches of the tree which length is superior to a fixed threshold. Since our skeletonization works in an iterative way, we possess local information for each branch. In this way, the symbolic description is facilitated and consists in a partitioning of the skeleton to collect the descriptive characteristics. Algorithms were implemented on the laboratory software platform (ArtiMED) developed in Borland C++ and estimated on digital and clinical data. The evaluation scheme adopts a specific experiment approach consisting in a comparison of the results of a series of 18 rotations of the initial volume

Description symbolique d'une arborescence vasculaire (application au réseau vasculaire cérébral)

Cette thèse s intéresse à la description symbolique d arborescences vasculaires issues d images 3D multimodales. Ce travail vise à fournir un cadre méthodologique global pour l analyse de telles structures et plus particulièrement pour l arbre vasculaire cérébral. Le domaine d application clinique visé est la neurochirurgie, notamment pour la planification du geste du praticien. Notre principale contribution est le développement d une méthode de squelettisation 3D, adaptée aux formes tubulaires et judicieuse pour la description symbolique. Nous proposons de baser la méthode de squelettisation sur la construction de l arbre de des plus courts chemins de Dijkstra. Ainsi, nous extrayons la branche principale qui correspond à la branche la plus longue de l arbre de Dijkstra, puis, nous détectons de manière itérative chaque branche annexe en conservant les branches de l arbre de longueur supérieure à un seuil fixé. Puisque notre squelettisation se réalise de manière itérative, nous possédons les informations locales à chacune des branches. De cette manière, la description symbolique est facilitée et consiste alors en un partitionnement du squelette permettant la collecte de ces informations. Les algorithmes ont été implémentés sous la plateforme logicielle du laboratoire, ArtiMed, et évalués sur données simulées et cliniques. L évaluation des méthodes de squelettisation et de description symbolique a fait l objet de l élaboration d un plan d expérience spécifique consistant en une comparaison des résultats sur une série de 18 rotations du volume initial.This thesis describes the methodology and the evaluation of a symbolic description method applied on vascular trees from multimodal 3D images. This work aims to supply a global methodological framework for the analysis of such structures and, more particularly, for the cerebral vascular tree. The clinical application field is neurosurgery and particularly neurosurgery planning. Our method is based on the application of the minimum cost-spanning tree using Dijkstra s algorithm and seems well appropriate to tubular objects. We skeletonize the structure in two stages: first, we extract the main branch which corresponds to the longest branch of the Dijkstra s tree, then, we detect iteratively every secondary branch by keeping the branches of the tree which length is superior to a fixed threshold. Since our skeletonization works in an iterative way, we possess local information for each branch. In this way, the symbolic description is facilitated and consists in a partitioning of the skeleton to collect the descriptive characteristics. Algorithms were implemented on the laboratory software platform (ArtiMED) developed in Borland C++ and estimated on digital and clinical data. The evaluation scheme adopts a specific experiment approach consisting in a comparison of the results of a series of 18 rotations of the initial volume.LILLE1-Bib. Electronique (590099901) / SudocSudocFranceF

Dijkstra's Algorithm Applied to 3D Skeletonization of the Brain Vascular Tree: Evaluation and Application to Symbolic Description

International audienceThis paper describes the methodology and the evaluation of a 3D skeletonization algorithm applied on brain vascular structure. This method is based on the application of the minimum cost-spanning tree using Dijkstra's algorithm and seems well appropriate to tubular objects. We briefly describe the different steps, from the segmentation to the skeleton analysis. Besides, we propose an original evaluation scheme of the method based on digital phantom and clinical data. The final aim of this work is to provide a symbolic description framework applied to cerebro-vascular structure

Three-dimensional skeletonization and symbolic description in vascular imaging: preliminary results

International audienceObjective : A general method was developed to analyze and describe tree-like structures needed for evaluation of complex morphology, such as the cerebral vascular tree. Clinical application of the method in neurosurgery includes planning of the surgeon's intraoperative gestures. Method : We have developed a 3D skeletonization method adapted to tubular forms with symbolic description. This approach implements an iterative Dijkstra minimum cost spanning tree, allowing a branch-by-branch skeleton extraction. The proposed method was implemented using the laboratory software platform (ArtiMed). The 3D skeleton approach was tested on simulated data and preliminary trials on clinical datasets mainly based on magnetic resonance image acquisitions. Results : A specific experimental evaluation plan was designed to test the skeletonization and symbolic description methods. Accuracy was tested by calculating the positioning error, and robustness was verified by comparing the results on a series of 18 rotations of the initial volume. Accuracy evaluation showed a Haussdorff's distance always smaller than 17 voxels and Dice's similarity coefficient greater than 70 %. Conclusion Our method of symbolic description enables the analysis and interpretation of a vascular network obtained from angiographic images. The method provides a simplified representation of the network in the form of a skeleton, as well as a description of the corresponding information in a tree-like view