Fully automated segmentation of the cervical cord from T1-weighted MRI using PropSeg: Application to multiple sclerosis.

Spinal cord (SC) atrophy, i.e. a reduction in the SC cross-sectional area (CSA) over time, can be measured by means of image segmentation using magnetic resonance imaging (MRI). However, segmentation methods have been limited by factors relating to reproducibility or sensitivity to change. The purpose of this study was to evaluate a fully automated SC segmentation method (PropSeg), and compare this to a semi-automated active surface (AS) method, in healthy controls (HC) and people with multiple sclerosis (MS). MRI data from 120 people were retrospectively analysed; 26 HC, 21 with clinically isolated syndrome, 26 relapsing remitting MS, 26 primary and 21 secondary progressive MS. MRI data from 40 people returning after one year were also analysed. CSA measurements were obtained within the cervical SC. Reproducibility of the measurements was assessed using the intraclass correlation coefficient (ICC). A comparison between mean CSA changes obtained with the two methods over time was performed using multivariate structural equation regression models. Associations between CSA measures and clinical scores were investigated using linear regression models. Compared to the AS method, the reproducibility of CSA measurements obtained with PropSeg was high, both in patients and in HC, with ICC > 0.98 in all cases. There was no significant difference between PropSeg and AS in terms of detecting change over time. Furthermore, PropSeg provided measures that correlated with physical disability, similar to the AS method. PropSeg is a time-efficient and reliable segmentation method, which requires no manual intervention, and may facilitate large multi-centre neuroprotective trials in progressive MS

Segmentation automatique de la moelle épinière sur des images de résonance magnétique par propagation de modèles déformables

RÉSUMÉ Les lésions de la moelle épinière, induites par des traumas (e.g. accident de la route) ou par des maladies neurodégénératives, touchent plus 85 000 personnes au Canada avec environ 4250 nouveaux cas chaque année1. Elles ont de plus un impact majeur sur la vie quotidienne des personnes atteintes, en provoquant des pertes de sensibilité et de contrôle moteur dont la gravité dépend de la taille et de l’emplacement des lésions. Bien qu’il existe des approches thérapeutiques permettant d’améliorer la réhabilitation fonctionnelle des patients, toutes ces approches se heurtent à une inconnue majeure : l’étendue des dégâts causés par les lésions. Un diagnostic précoce et précis des maladies neurodégénératives touchant la moelle épinière permettrait d’améliorer grandement l’efficacité de leurs traitements. Depuis de nombreuses années, l’IRM a prouvé son potentiel dans le diagnostic et le pronostic des lésions de la moelle épinière (Cadotte, 2011; Cohen-Adad et al., 2011). Ce domaine manque cependant encore d’outils complètement automatisés permettant l’extraction et la comparaison de métriques cliniques reliées à la structure de la moelle (aire de section transverse, volume, etc.). La segmentation de la moelle épinière sur des images IRM anatomiques peut fournir des mesures d’aires et de volumes de la moelle (Losseff et al., 1996) et peut quantifier son atrophie en cas de maladies neurodégénératives telles que la sclérose en plaques (Chen et al., 2013) et la sclérose latérale amyotrophique (Cohen-Adad et al., 2011). Ce projet de maîtrise vise à développer une méthode de segmentation complètement automatique de la moelle épinière, fonctionnant sur plusieurs types d’images IRM (pondérées en T1 et en T2) et sur n’importe quel champ de vue (cervical ou thoracique), et permettant d’extraire et de comparer des mesures précises de la moelle épinière. La revue de la littérature a permis de mettre en évidence le manque de méthode de segmentation automatique de la moelle épinière fonctionnant sur n’importe quel type de contraste et de champ de vue. Elle a toutefois fait ressortir une série de propriétés intéressantes, dans les méthodes semi-automatiques existantes, pouvant être combinées pour former une méthode complètement automatisée.----------ABSTRACT Spinal cord lesions affects more than 85,000 people in Canada with about 4,250 new cases every year. Lesions can be caused by traumatic injuries or by neurodegenerative diseases such as multiple sclerosis. They have an important impact on a patient’s daily life, inducing loss of sensibility or motor control in the human body. The extent of damages caused by a lesion varies with the number of damaged spinal cord tracks, and depends on the size and the position of the lesion within the spinal cord. Although therapeutic approaches for patient functional rehabilitation exist, they all face an unknown variable: the extent of spinal cord lesions. A precise and early diagnosis of neurodegenerative diseases would improve their treatment efficiency. For a number of years, MRI has demonstrated its potential in the diagnosis and prognosis of spinal cord lesions (Cadotte, 2011; Cohen-Adad et al., 2010). However, this research field still lacks of fully automatized tools for the extraction and comparison of clinical metrics related to the spinal cord structure (e.g. cross-sectional area, volumes). Spinal cord segmentation on anatomical MR images can provide accurate area and volume measurements (Losseff et al., 1996) and could quantify spinal cord atrophy caused by neurodegenerative diseases such as multiple sclerosis (Chen et al., 2013) or amyotrophic lateral sclerosis (Cohen-Adad et al., 2011). The objective of this Master’s project is to develop a fully automatic spinal cord segmentation method, working on multiple MR contrasts and any field of view, able to extract and compare accurate spinal cord measurements. The literature review pointed out the lack of such a method but highlighted several interesting features in existing methods, that can be combined to develop a new automatic segmentation algorithm. The method developed in this project is based on the multi-resolution propagation of a deformable model. First, the spinal cord position and orientation is detected in the image using an elliptical Hough transform on multiple adjacent axial slices. A low-resolution tubular mesh is then build around the detection point and direction and deformed on spinal cord edges by minimizing an energy equation. An iterative process, composed by the duplication, translation, orientation and deformation of the mesh, propagates the surface along the spinal cord. Finally, a refinement and a global deformation of the surface provide accurate segmentation of the spinal cord. Measurements can be directly extracted from the segmentation surface. The spinal canal can also be segmented with our method by simply inversing the gradient in the image an

Modeling and visualization of medical anesthesiology acts

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaIn recent years, medical visualization has evolved from simple 2D images on a light board to 3D computarized images. This move enabled doctors to find better ways of planning surgery and to diagnose patients. Although there is a great variety of 3D medical imaging software, it falls short when dealing with anesthesiology acts. Very little anaesthesia related work has been done. As a consequence, doctors and medical students have had little support to study the subject of anesthesia in the human body. We all are aware of how costly can be setting medical experiments, covering not just medical aspects but ethical and financial ones as well. With this work we hope to contribute for having better medical visualization tools in the area of anesthesiology. Doctors and in particular medical students should study anesthesiology acts more efficiently. They should be able to identify better locations to administrate the anesthesia, to study how long does it take for the anesthesia to affect patients, to relate the effect on patients with quantity of anaesthesia provided, etc. In this work, we present a medical visualization prototype with three main functionalities: image pre-processing, segmentation and rendering. The image pre-processing is mainly used to remove noise from images, which were obtained via imaging scanners. In the segmentation stage it is possible to identify relevant anatomical structures using proper segmentation algorithms. As a proof of concept, we focus our attention in the lumbosacral region of the human body, with data acquired via MRI scanners. The segmentation we provide relies mostly in two algorithms: region growing and level sets. The outcome of the segmentation implies the creation of a 3D model of the anatomical structure under analysis. As for the rendering, the 3D models are visualized using the marching cubes algorithm. The software we have developed also supports time-dependent data. Hence, we could represent the anesthesia flowing in the human body. Unfortunately, we were not able to obtain such type of data for testing. But we have used human lung data to validate this functionality

Quantification of spinal cord atrophy in magnetic resonance images

Quantifying the volume of the spinal cord is of vital interest for studying and understanding diseases of the central nervous system such as multiple sclerosis (MS). In this thesis, which is motivated by MS research, we propose methods for measuring the spinal cord cross-sectional area and volume in magnetic resonance (MR) images. These measurements are used for determining neural atrophy and for performing both longitudinal and cross-sectional comparisons in clinical trials. We present three evolutionary steps of our approach: In the first step, we use graph cut–based image segmentation on the intensities of T1-weighted MR images. In the second step, we combine a continuous max flow segmentation algorithm with a cross-sectional similarity prior and Hessian-based structural features, which we apply to T1- and T2-weighted images. The prior leverages the fact that the spinal cord is an elongated structure by constraining its cross-sectional shape to vary only slowly along one image axis. In conjunction with the additional features, the segmentation robustness is thus increased. In the third step, we combine continuous max flow with anisotropic total variation regularization, which enables us to direct the regularization of the cross-sectional shape of the spinal cord more flexibly. We implement the proposed approach as a semi-automatic software toolchain that automatically segments the spinal cord, reconstructs its surface, and acquires the desired measurements. The software employs a user-provided anatomical landmark as well as hints for the location of the spinal cord and its surroundings. It accounts for the bending of the spine, MR-induced image distortions, and noise. We evaluate the proposed methods in experiments on phantom, healthy subject, and patient data. Our measurement accuracy and precision are on par with the state of the art. At the same time, our measurements on MS patient data are in accordance with the medical literature

Development of an MRI Template and Analysis Pipeline for the Spinal Cord and Application in Patients with Spinal Cord Injury

La moelle épinière est un organe fondamental du corps humain. Étant le lien entre le cerveau et le système nerveux périphérique, endommager la moelle épinière, que ce soit suite à un trauma ou une maladie neurodégénérative, a des conséquences graves sur la qualité de vie des patients. En effet, les maladies et traumatismes touchant la moelle épinière peuvent affecter l’intégrité des neurones et provoquer des troubles neurologiques et/ou des handicaps fonctionnels. Bien que de nombreuses voies thérapeutiques pour traiter les lésions de la moelle épinière existent, la connaissance de l’étendue des dégâts causés par ces lésions est primordiale pour améliorer l’efficacité de leur traitement et les décisions cliniques associées. L’imagerie par résonance magnétique (IRM) a démontré un grand potentiel pour le diagnostic et pronostic des maladies neurodégénératives et traumas de la moelle épinière. Plus particulièrement, l’analyse par template de données IRM du cerveau, couplée à des outils de traitement d’images automatisés, a permis une meilleure compréhension des mécanismes sous-jacents de maladies comme l’Alzheimer et la Sclérose en Plaques. Extraire automatiquement des informations pertinentes d’images IRM au sein de régions spécifiques de la moelle épinière présente toutefois de plus grands défis que dans le cerveau. Il n’existe en effet qu’un nombre limité de template de la moelle épinière dans la littérature, et aucun ne couvre toute la moelle épinière ou n’est lié à un template existant du cerveau. Ce manque de template et d’outils automatisés rend difficile la tenue de larges études d’analyse de la moelle épinière sur des populations variées. L’objectif de ce projet est donc de proposer un nouveau template IRM couvrant toute la moelle épinière, recalé avec un template existant du cerveau, et intégrant des atlas de la structure interne de la moelle épinière (e.g., matière blanche et grise, tracts de la matière blanche). Ce template doit venir avec une série d’outils automatisés permettant l’extraction d’information IRM au sein de régions spécifiques de la moelle épinière. La question générale de recherche de ce projet est donc « Comment créer un template générique de la moelle épinière, qui permettrait l’analyse non biaisée et reproductible de données IRM de la moelle épinière ? ». Plusieurs contributions originales ont été proposées pour répondre à cette question et vont être décrites dans les prochains paragraphes. La première contribution de ce projet est le développement du logiciel Spinal Cord Toolbox (SCT). SCT est un logiciel open-source de traitement d’images IRM multi-parametrique de la moelle épinière (De Leener, Lévy, et al., 2016). Ce logiciel intègre notamment des outils pour la détection et la segmentation automatique de la moelle épinière et de sa structure interne (i.e., matière blanche et matière grise), l’identification et la labellisation des niveaux vertébraux, le recalage d’images IRM multimodales sur un template générique de la moelle épinière (précédemment le template MNI-Poly-AMU, maintenant le template PAM50, proposé içi). En se basant sur un atlas de la moelle, SCT intègre également des outils pour extraire des données IRM de régions spécifiques de la moelle épinière, comme la matière blanche et grise et les tracts de la matière blanche, ainsi que sur des niveaux vertébraux spécifiques. D’autres outils additionnels ont aussi été proposés, comme des outils de correction de mouvement et de traitement basiques d’images appliqués le long de la moelle épinière. Chaque outil intégré à SCT a été validé sur un jeu de données multimodales. La deuxième contribution de ce projet est le développement d’une nouvelle méthode de recalage d’images IRM de la moelle épinière (De Leener, Mangeat, et al., 2017). Cette méthode a été développée pour un usage particulier : le redressement d’images IRM de la moelle épinière, mais peut également être utilisé pour recaler plusieurs images de la moelle épinière entre elles, tout en tenant compte de la distribution vertébrale de chaque sujet. La méthode proposée se base sur une approximation globale de la courbure de la moelle épinière dans l’espace et sur la résolution analytique des champs de déformation entre les deux images. La validation de cette nouvelle méthode a été réalisée sur une population de sujets sains et de patients touchés par une compression de la moelle épinière. La contribution majeure de ce projet est le développement d’un système de création de template IRM de la moelle épinière et la proposition du template PAM50 comme template de référence pour les études d’analyse par template de données IRM de la moelle épinière. Le template PAM50 a été créé à partir d’images IRM tiré de 50 sujets sains, et a été généré en utilisant le redressement d’images présenté ci-dessus et une méthode de recalage d’images itératif non linéaire, après plusieurs étapes de prétraitement d’images. Ces étapes de prétraitement incluent la segmentation automatique de la moelle épinière, l’extraction manuelle du bord antérieur du tronc cérébral, la détection et l’identification des disques intervertébraux, et la normalisation d’intensité le long de la moelle. Suite au prétraitement, la ligne centrale moyenne de la moelle et la distribution vertébrale ont été calculées sur la population entière de sujets et une image initiale de template a été générée. Après avoir recalé toutes les images sur ce template initial, le template PAM50 a été créé en utilisant un processus itératif de recalage d’image, utilisé pour générer des templates de cerveau. Le PAM50 couvre le tronc cérébral et la moelle épinière en entier, est disponible pour les contrastes IRM pondérés en T1, T2 et T2*, et intègre des cartes probabilistes et atlas de la structure interne de la moelle épinière. De plus, le PAM50 a été recalé sur le template ICBM152 du cerveau, permettant ainsi la tenue d’analyse par template simultanément dans le cerveau et dans la moelle épinière. Finalement, plusieurs résultats complémentaires ont été présentés dans cette dissertation. Premièrement, une étude de validation de la répétabilité et reproductibilité de mesures de l’aire de section de la moelle épinière a été menée sur une population de patients touchés par la sclérose en plaques. Les résultats démontrent une haute fiabilité des mesures ainsi que la possibilité de détecter des changements très subtiles de l’aire de section transverse de la moelle, importants pour mesurer l’atrophie de la moelle épinière précoce due à des maladies neurodégénératives comme la sclérose en plaques. Deuxièmement, un nouveau biomarqueur IRM des lésions de la moelle épinière a été proposé, en collaboration avec Allan Martin, de l’Université de Toronto. Ce biomarqueur, calculé à partir du ratio d’intensité entre la matière blanche et grise sur des images IRM pondérées en T2*, utilise directement les développements proposés dans ce projet, notamment en utilisant le recalage du template de la moelle épinière et les méthodes de segmentation de la moelle. La faisabilité d’extraire des mesures de données IRM multiparamétrique dans des régions spécifiques de la moelle épinière a également été démontrée, permettant d’améliorer le diagnostic et pronostic de lésions et compression de la moelle épinière. Finalement, une nouvelle méthode d’extraction de la morphométrie de la moelle épinière a été proposée et utilisée sur une population de patients touchés par une compression asymptomatique de la moelle épinière, démontrant de grandes capacités de diagnostic (> 99%). Le développement du template PAM50 comble le manque de template de la moelle épinière dans la littérature mais présente cependant plusieurs limitations. En effet, le template proposé se base sur une population de 50 sujets sains et jeunes (âge moyen = 27 +- 6.5) et est donc biaisée vers cette population particulière. Adapter les analyses par template pour un autre type de population (âge, race ou maladie différente) peut être réalisé directement sur les méthodes d’analyse mais aussi sur le template en lui-même. Tous le code pour générer le template a en effet été mis en ligne (https://github.com/neuropoly/template) pour permettre à tout groupe de recherche de développer son propre template. Une autre limitation de ce projet est le choix d’un système de coordonnées basé sur la position des vertèbres. En effet, les vertèbres ne représentent pas complètement le caractère fonctionnel de la moelle épinière, à cause de la différence entre les niveaux vertébraux et spinaux. Le développement d’un système de coordonnées spinal, bien que difficile à caractériser dans des images IRM, serait plus approprié pour l’analyse fonctionnelle de la moelle épinière. Finalement, il existe encore de nombreux défis pour automatiser l’ensemble des outils développés dans ce projet et les rendre robuste pour la majorité des contrastes et champs de vue utilisés en IRM conventionnel et clinique. Ce projet a présenté plusieurs développements importants pour l’analyse de données IRM de la moelle épinière. De nombreuses améliorations du travail présenté sont cependant requises pour amener ces outils dans un contexte clinique et pour permettre d’améliorer notre compréhension des maladies affectant la moelle épinière. Les applications cliniques requièrent notamment l’amélioration de la robustesse et de l’automatisation des méthodes d’analyse d’images proposées. La caractérisation de la structure interne de la moelle épinière, incluant la matière blanche et la matière grise, présente en effet de grands défis, compte tenu de la qualité et la résolution des images IRM standard acquises en clinique. Les outils développés et validés au cours de ce projet ont un grand potentiel pour la compréhension et la caractérisation des maladies affectant la moelle épinière et aura un impact significatif sur la communauté de la neuroimagerie.----------ABSTRACT The spinal cord plays a fundamental role in the human body, as part of the central nervous system and being the vector between the brain and the peripheral nervous system. Damaging the spinal cord, through traumatic injuries or neurodegenerative diseases, can significantly affect the quality of life of patients. Indeed, spinal cord injuries and diseases can affect the integrity of neurons, and induce neurological impairments and/or functional disabilities. While various treatment procedures exist, assessing the extent of damages and understanding the underlying mechanisms of diseases would improve treatment efficiency and clinical decisions. Over the last decades, magnetic resonance imaging (MRI) has demonstrated a high potential for the diagnosis and prognosis of spinal cord injury and neurodegenerative diseases. Particularly, template-based analysis of brain MRI data has been very helpful for the understanding of neurological diseases, using automated analysis of large groups of patients. However, extracting MRI information within specific regions of the spinal cord with minimum bias and using automated tools is still a challenge. Indeed, only a limited number of MRI template of the spinal cord exists, and none covers the full spinal cord, thereby preventing large multi-centric template-based analysis of the spinal cord. Moreover, no template integrates both the spinal cord and the brain region, thereby preventing simultaneous cerebrospinal studies. The objective of this project was to propose a new MRI template of the full spinal cord, which allows simultaneous brain and spinal cord studies, that integrates atlases of the spinal cord internal structures (e.g., white and gray matter, white matter pathways) and that comes with tools for extracting information within these subregions. More particularly, the general research question of the project was “How to create generic MRI templates of the spinal cord that would enable unbiased and reproducible template-based analysis of spinal cord MRI data?”. Several original contributions have been made to answer this question and to enable template-based analysis of spinal cord MRI data. The first contribution was the development of the Spinal Cord Toolbox (SCT), a comprehensive and open-source software for processing multi-parametric MRI data of the spinal cord (De Leener, Lévy, et al., 2016). SCT includes tools for the automatic segmentation of the spinal cord and its internal structure (white and gray matter), vertebral labeling, registration of multimodal MRI data (structural and non-structural) on a spinal cord MRI template (initially the MNI-Poly-AMU template, later the PAM50 template), co-registration of spinal cord MRI images, as well as the robust extraction of MRI metric within specific regions of the spinal cord (i.e., white and gray matter, white matter tracts, gray matter subregions) and specific vertebral levels using a spinal cord atlas (Lévy et al., 2015). Additional tools include robust motion correction and image processing along the spinal cord. Each tool included in SCT has been validated on a multimodal dataset. The second contribution of this project was the development of a novel registration method dedicated to spinal cord images, with an interest in the straightening of the spinal cord, while preserving its topology (De Leener, Mangeat et al., 2017). This method is based on the global approximation of the spinal cord and the analytical computation of deformation fields perpendicular to the centerline. Validation included calculation of distance measurements after straightening on a population of healthy subjects and patients with spinal cord compression. The major contribution of this project was the development of a framework for generating MRI template of the spinal cord and the PAM50 template, an unbiased and symmetrical MRI template of the brainstem and full spinal cord. Based on 50 healthy subjects, the PAM50 template was generated using an iterative nonlinear registration process, after applying normalization and straightening of all images. Pre-processing included segmentation of the spinal cord, manual delineation of the brainstem anterior edge, detection and identification of intervertebral disks, and normalization of intensity along the spinal cord. Next, the average centerline and vertebral distribution was computed to create an initial straight template space. Then, all images were registered to the initial template space and an iterative nonlinear registration framework was applied to create the final symmetrical template. The PAM50 covers the brainstem and the full spinal cord, from C1 to L2, is available for T1-, T2- and T2*-weighted contrasts, and includes probabilistic maps of the white and the gray matter and atlases of the white matter pathways and gray matter subregions. Additionally, the PAM50 template has been merged with the ICBM152 brain template, thereby allowing for simultaneous cerebrospinal template-based analysis. Finally, several complementary results, focused on clinical validation and applications, are presented. First, a reproducibility and repeatability study of cross-sectional area measurements using SCT (De Leener, Granberg, Fink, Stikov, & Cohen-Adad, 2017) was performed on a Multiple Sclerosis population (n=9). The results demonstrated the high reproducibility and repeatability of SCT and its ability to detect very subtle atrophy of the spinal cord. Second, a novel biomarker of spinal cord injury has been proposed. Based on the T2*-weighted intensity ratio between the white and the gray matter, this new biomarker is computed by registering MRI images with the PAM50 template and extracting metrics using probabilistic atlases. Additionally, the feasibility of extracting multiparametric MRI metrics from subregions of the spinal cord has been demonstrated and the diagnostic potential of this approach has been assessed on a degenerative cervical myelopathy (DCM) population. Finally, a method for extracting shape morphometrics along the spinal cord has been proposed, including spinal cord flattening, indentation and torsion. These metrics demonstrated high capabilities for the diagnostic of asymptomatic spinal cord compression (AUC=99.8% for flattening, 99.3% for indentation, and 98.4% for torsion). The development of the PAM50 template enables unbiased template-based analysis of the spinal cord. However, the PAM50 template has several limitations. Indeed, the proposed template has been generated with multimodal MRI images from 50 healthy and young individuals (age = 27+/- 6.5 y.o.). Therefore, the template is specific to this particular population and could not be directly usable for age- or disease-specific populations. One solution is to open-source the templategeneration code so that research groups can generate and use their own spinal cord MRI template. The code is available on https://github.com/neuropoly/template. While this project introduced a generic referential coordinate system, based on vertebral levels and the pontomedullary junction as origin, one limitation is the choice of this coordinate system. Another coordinate system, based spinal segments would be more suitable for functional analysis. However, the acquisition of MRI images with high enough resolution to delineate the spinal roots is still challenging. Finally, several challenges in the automation of spinal cord MRI processing remains, including the robust detection and identification of vertebral levels, particularly in case of small fields-of-view. This project introduced key developments for the analysis of spinal cord MRI data. Many more developments are still required to bring them into clinics and to improve our understanding of diseases affecting the spinal cord. Indeed, clinical applications require the improvement of the robustness and the automation of the proposed processing and analysis tools. Particularly, the detection and segmentation of spinal cord structures, including vertebral labeling and white/gray matter segmentation, is still challenging, given the lowest quality and resolution of standard clinical MRI acquisition. The tools developed and validated here have the potential to improve our understanding and the characterization of diseases affecting the spinal cord and will have a significant impact on the neuroimaging community

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

Procedures for finite element mesh generation from medical imaging: application to the intervertebral disc

Dissertação de mestrado integrado em Engenharia BiomédicaThe paramount goal of this ‘half-year’ work is the development of a set of methodologies and procedures for the geometric modelling by a finite element (FE) mesh of the bio-structure of a motion segment (or functional spinal unit), i.e., two vertebrae and an intervertebral disc, from segmented medical images (processed from medical imaging). At an initial stage, a three-dimensional voxel-based geometric model of a goat motion segment was created from magnetic resonance imaging (MRI) data. An imaging processing software (ScanIP/Simplewire) was used for imaging segmentation (identification of different structures and tissues), both in images with lower (normal MRI) and higher (micro-MRI) resolutions. It shall be noticed that some soft-tissues, such as annulus fibrosus or nucleus pulposus, are very hard to isolate and identify given that the interface between them is not clearly defined. At the end of this stage, images with different resolutions allowed to generate different 3D voxel-based geometric models. Thereafter, a procedure for the FE mesh generation from the aforementioned voxelized data should be studied and applied. However, as the original geometry was only approximately known from real medical imaging, it was difficult to objectively quantify the quality of the FE meshing procedure and the accuracy between source geometry and target FE mesh. In order to overcome such difficulties, and due to the lack of quality of the available medical imaging, a “virtualization” procedure was developed to create a set of segmented 2D medical images from a well-defined geometry of a motion segment. The main idea was to create the conditions to quantify the quality and the accuracy of the developed FE meshing procedure, as well to study the effect of imaging resolution. Starting from the virtually generated 2D segmented images, a 3D voxel-based structure was achieved. Given that initial domains are now clearly defined, there is no need for further image processing. Then, a two-step FE mesh generation procedure (generation followed by simplification) allows to create an optimized tetrahedral FE mesh directly from 3D voxelized data. Finally, because the virtualization procedure allowed to know the initial geometry, one is able to objectively quantify the quality and the accuracy of the final simplified tetrahedral FE mesh, and thus to understand and quantify: a) the role of the medical image resolution on the FE geometrical reconstruction, b) the procedure and parameters of the FE mesh generation step, and c) the procedure and parameters of the FE mesh simplification step, and thus to give a clear contribution in the definition of the procedure for the FE mesh generation from medical imaging in case of an intervertebral disc.O objetivo fundamental deste trabalho de seis meses é o desenvolvimento de um conjunto de metodologias e procedimentos para a modelação geométrica, através de uma malha de elementos finitos (EF) de uma bio-estrutura de um motion segment (ou unidade funcional da coluna), ou seja, duas vértebras e um disco intervertebral, a partir de imagens médicas segmentadas (processadas a partir de imagiologia médica). Numa fase inicial, um modelo geométrico tridimensional baseado em voxels de um motion segment de uma cabra foi criado a partir de informação de imagens médicas de ressonância magnética (RM). Um software de processamento de imagem (ScanIp/Simplewire) foi usado para segmentação de imagens (identificação de diferentes estruturas e tecidos), em imagens de menor (RM normal) e maior (micro-RM) resolução. Deve ser referido que alguns tecidos moles, como o anel fibroso e o núcleo pulposo são muito difíceis de isolar e identificar, dado que as fronteiras destes não estão claramente definidas. No final desta etapa, as imagens com diferentes resoluções permitiram gerar diferentes modelos geométricos 3D baseados em voxels. Posteriormente, um procedimento para geração de malha de EF, a partir da informação voxelizada acima mencionada, deveria ser estudado e aplicado. No entanto, como a geometria original era aproximadamente conhecida a partir de imagens médicas reais, foi difícil quantificar objetivamente a qualidade do procedimento de geração de malha de EF e a precisão entre a geometria de origem e a malha de EF de destino. A fim de superar tais dificuldades, e devido à falta de qualidade de imagens médicas disponíveis, um procedimento de “virtualização” foi desenvolvido para criar um conjunto de imagens médicas 2D segmentadas a partir de uma geometria de um motion segment bem conhecida. A principal ideia foi criar as condições para quantificar a qualidade e a precisão do procedimento de geração de malha de EF desenvolvido, bem como estudar o efeito da resolução da imagem médica. A partir das imagens 2D segmentadas, geradas virtualmente, uma estrutura de voxels 3D pode ser conseguida. Dado que os domínios iniciais estão agora claramente definidos, não há necessidade de processamento de imagem adicional. Por conseguinte, um procedimento de geração de malha de EF de duas etapas (geração seguida por simplificação) permite criar uma malha de EF tetraédrica otimizada diretamente a partir de informação 3D voxelizada. Por fim, como o procedimento de virtualização permitiu conhecer a geometria inicial, é possível quantificar objetivamente a qualidade e exatidão da malha de EF tetraédrica final simplificada, e assim, compreender e quantificar: a) o papel da resolução da imagem médica na reconstrução geométrica de EF; b) o procedimento e os parâmetros da etapa de geração de malha de EF; c) o procedimento e os parâmetros da etapa de simplificação de malhas de EF, e assim, dar uma contribuição clara na definição do procedimento para a geração de malha de EF a partir de imagem médica, no caso de um disco intervertebral.European Project : NP Mimetic - Biomimetic Nano-Fiber Based Nucleus Pulposus Regeneration for the Treatment of Degenerative Disc Disease, funded by the European Commission under FP7 (grant NMP3-SL-2010-246351