Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

Aquatics reconstruction software: the design of a diagnostic tool based on computer vision algorithms

Computer vision methods can be applied to a variety of medical and surgical applications, and many techniques and algorithms are available that can be used to recover 3D shapes and information from images range and volume data. Complex practical applications, however, are rarely approachable with a single technique, and require detailed analysis on how they can be subdivided in subtasks that are computationally treatable and that, at the same time, allow for the appropriate level of user-interaction. In this paper we show an example of a complex application where, following criteria of efficiency, reliability and user friendliness, several computer vision techniques have been selected and customized to build a system able to support diagnosis and endovascular treatment of Abdominal Aortic Aneurysms. The system reconstructs the geometrical representation of four different structures related to the aorta (vessel lumen, thrombus, calcifications and skeleton) from CT angiography data. In this way it supports the three dimensional measurements required for a careful geometrical evaluation of the vessel, that is fundamental to decide if the treatment is necessary and to perform, in this case, its planning. The system has been realized within the European trial AQUATICS (IST-1999-20226 EUTIST-M WP 12), and it has been widely tested on clinical data

Minimax Estimation of Distances on a Surface and Minimax Manifold Learning in the Isometric-to-Convex Setting

We start by considering the problem of estimating intrinsic distances on a smooth surface. We show that sharper estimates can be obtained via a reconstruction of the surface, and discuss the use of the tangential Delaunay complex for that purpose. We further show that the resulting approximation rate is in fact optimal in an information-theoretic (minimax) sense. We then turn to manifold learning and argue that a variant of Isomap where the distances are instead computed on a reconstructed surface is minimax optimal for the problem of isometric manifold embedding

Medial Axis Approximation and Regularization

Medial axis is a classical shape descriptor. Among many good properties, medial axis is thin, centered in the shape, and topology preserving. Therefore, it is constantly sought after by researchers and practitioners in their respective domains. However, two barriers remain that hinder wide adoption of medial axis. First, exact computation of medial axis is very difficult. Hence, in practice medial axis is approximated discretely. Though abundant approximation methods exist, they are either limited in scalability, insufficient in theoretical soundness, or susceptible to numerical issues. Second, medial axis is easily disturbed by small noises on its defining shape. A majority of current works define a significance measure to prune noises on medial axis. Among them, local measures are widely available due to their efficiency, but can be either too aggressive or conservative. While global measures outperform local ones in differentiating noises from features, they are rarely well-defined or efficient to compute. In this dissertation, we attempt to address these issues with sound, robust and efficient solutions. In Chapter 2, we propose a novel medial axis approximation called voxel core. We show voxel core is topologically and geometrically convergent to the true medial axis. We then describe a straightforward implementation as a result of our simple definition. In a variety of experiments, our method is shown to be efficient and robust in delivering topological promises on a wide range of shapes. In Chapter 3, we present Erosion Thickness (ET) to regularize instability. ET is the first global measure in 3D that is well-defined and efficient to compute. To demonstrate its usefulness, we utilize ET to generate a family of shape revealing and topology preserving skeletons. Finally, we point out future directions, and potential applications of our works in real world problems

Vascular Modeling from Volumetric Diagnostic Data: A Review

Reconstruction of vascular trees from digital diagnostic images is a challenging task in the development of tools for simulation and procedural planning for clinical use. Improvements in quality and resolution of acquisition modalities are constantly increasing the fields of application of computer assisted techniques for vascular modeling and a lot of Computer Vision and Computer Graphics research groups are currently active in the field, developing methodologies, algorithms and software prototypes able to recover models of branches of human vascular system from different kinds of input images. Reconstruction methods can be extremely different according to image type, accuracy requirements and level of automation. Some technologies have been validated and are available on medical workstation, others have still to be validated in clinical environments. It is difficult, therefore, to give a complete overview of the different approach used and results obtained, this paper just presents a short review including some examples of the principal reconstruction approaches proposed for vascular reconstruction, showing also the contribution given to the field by the Medical Application Area of CRS4, where methods to recover vascular models have been implemented and used for blood flow analysis, quantitative diagnosis and surgical planning tools based on Virtual Reality

Accurate geometry reconstruction of vascular structures using implicit splines

3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy

Reconstruction de formes tubulaires à partir de nuages de points : application à l’estimation de la géométrie forestière

Les capacités des technologies de télédétection ont augmenté exponentiellement au cours des dernières années : de nouveaux scanners fournissent maintenant une représentation géométrique de leur environnement sous la forme de nuage de points avec une précision jusqu'ici inégalée. Le traitement de nuages de points est donc devenu une discipline à part entière avec ses problématiques propres et de nombreux défis à relever. Le coeur de cette thèse porte sur la modélisation géométrique et introduit une méthode robuste d'extraction de formes tubulaires à partir de nuages de points. Nous avons choisi de tester nos méthodes dans le contexte applicatif difficile de la foresterie pour mettre en valeur la robustesse de nos algorithmes et leur application à des données volumineuses. Nos méthodes intègrent les normales aux points comme information supplémentaire pour atteindre les objectifs de performance nécessaire au traitement de nuages de points volumineux.Cependant, ces normales ne sont généralement pas fournies par les capteurs, il est donc nécessaire de les pré-calculer.Pour préserver la rapidité d'exécution, notre premier développement a donc consisté à présenter une méthode rapide d'estimation de normales. Pour ce faire nous avons approximé localement la géométrie du nuage de points en utilisant des "patchs" lisses dont la taille s'adapte à la complexité locale des nuages de points. Nos travaux se sont ensuite concentrés sur l’extraction robuste de formes tubulaires dans des nuages de points denses, occlus, bruités et de densité inhomogène. Dans cette optique, nous avons développé une variante de la transformée de Hough dont la complexité est réduite grâce aux normales calculées. Nous avons ensuite couplé ces travaux à une proposition de contours actifs indépendants de leur paramétrisation. Cette combinaison assure la cohérence interne des formes reconstruites et s’affranchit ainsi des problèmes liés à l'occlusion, au bruit et aux variations de densité. Notre méthode a été validée en environnement complexe forestier pour reconstruire des troncs d'arbre afin d'en relever les qualités par comparaison à des méthodes existantes. La reconstruction de troncs d'arbre ouvre d'autres questions à mi-chemin entre foresterie et géométrie. La segmentation des arbres d'une placette forestière est l'une d’entre elles. C'est pourquoi nous proposons également une méthode de segmentation conçue pour contourner les défauts des nuages de points forestiers et isoler les différents objets d'un jeu de données. Durant nos travaux nous avons utilisé des approches de modélisation pour répondre à des questions géométriques, et nous les avons appliqué à des problématiques forestières.Il en résulte un pipeline de traitements cohérent qui, bien qu'illustré sur des données forestières, est applicable dans des contextes variés.Abstract : The potential of remote sensing technologies has recently increased exponentially: new sensors now provide a geometric representation of their environment in the form of point clouds with unrivalled accuracy. Point cloud processing hence became a full discipline, including specific problems and many challenges to face. The core of this thesis concerns geometric modelling and introduces a fast and robust method for the extraction of tubular shapes from point clouds. We hence chose to test our method in the difficult applicative context of forestry in order to highlight the robustness of our algorithms and their application to large data sets. Our methods integrate normal vectors as a supplementary geometric information in order to achieve the performance goal necessary for large point cloud processing. However, remote sensing techniques do not commonly provide normal vectors, thus they have to be computed. Our first development hence consisted in the development of a fast normal estimation method on point cloud in order to reduce the computing time on large point clouds. To do so, we locally approximated the point cloud geometry using smooth ''patches`` of points which size adapts to the local complexity of the point cloud geometry. We then focused our work on the robust extraction of tubular shapes from dense, occluded, noisy point clouds suffering from non-homogeneous sampling density. For this objective, we developed a variant of the Hough transform which complexity is reduced thanks to the computed normal vectors. We then combined this research with a new definition of parametrisation-invariant active contours. This combination ensures the internal coherence of the reconstructed shapes and alleviates issues related to occlusion, noise and variation of sampling density. We validated our method in complex forest environments with the reconstruction of tree stems to emphasize its advantages and compare it to existing methods. Tree stem reconstruction also opens new perspectives halfway in between forestry and geometry. One of them is the segmentation of trees from a forest plot. Therefore we also propose a segmentation approach designed to overcome the defects of forest point clouds and capable of isolating objects inside a point cloud. During our work we used modelling approaches to answer geometric questions and we applied our methods to forestry problems. Therefore, our studies result in a processing pipeline adapted to forest point cloud analyses, but the general geometric algorithms we propose can also be applied in various contexts