Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach

Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of many adults. It a ects almost 1:5 - 5% of the general population. Sub- Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of morbidity and mortality. Therefore, radiologists aim to detect it and diagnose it at an early stage, by analyzing the medical images, to prevent or reduce its damages. The analysis process is traditionally done manually. However, with the emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are adopted in the clinics to overcome the traditional process disadvantages, as the dependency of the radiologist's experience, the inter and intra observation variability, the increase in the probability of error which increases consequently with the growing number of medical images to be analyzed, and the artifacts added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA, etc.) which impedes the radiologist' s work. Due to the aforementioned reasons, many research works propose di erent segmentation approaches to automate the analysis process of detecting a CA using complementary segmentation techniques; but due to the challenging task of developing a robust reproducible reliable algorithm to detect CA regardless of its shape, size, and location from a variety of the acquisition methods, a diversity of proposed and developed approaches exist which still su er from some limitations. This thesis aims to contribute in this research area by adopting two promising techniques based on the multiresolution and statistical approaches in the Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform (CT), which empowers the segmentation by extracting features not apparent in the normal image scale. While the second technique is the Hidden Markov Random Field model with Expectation Maximization (HMRF-EM), which segments the image based on the relationship of the neighboring pixels in the contourlet domain. The developed algorithm reveals promising results on the four tested Three- Dimensional Rotational Angiography (3D RA) datasets, where an objective and a subjective evaluation are carried out. For the objective evaluation, six performance metrics are adopted which are: accuracy, Dice Similarity Index (DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city, and sensitivity. As for the subjective evaluation, one expert and four observers with some medical background are involved to assess the segmentation visually. Both evaluations compare the segmented volumes against the ground truth data

An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work, Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for di erent k. Then, the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness function. The clustered boundaries of the tissue classes further enhanced by morphological operations. The FC technique is hybridized with the standard image post-processing techniques such as marker controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets (DRLS). This procedure is implemented on renowned BRATS challenge dataset of di erent modalities and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of the slices is performed using the metadata information present in the DICOM header. The validation of the segmentation procedures against the ground truth images authorizes that the segmented objects of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity coe cients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D visualization and tumor volume estimation are done using accessible DICOM information.Ministerio de Desarrollo de Recursos Humanos, India SPARC/2018-2019/P145/SLUniversidad Politécnica de Tomsk, Rusia RRSG/19/500

BRAIM: A computer-aided diagnosis system for neurodegenerative diseases and brain lesion monitoring from volumetric analyses

[EN] Background and objective: This paper presents BRAIM, a computer-aided diagnosis (CAD) system to help clinicians in diagnosing and treatment monitoring of brain diseases from magnetic resonance image processing. BRAIM can be used for early diagnosis of neurodegenerative diseases such as Parkinson, Alzheimer or Multiple Sclerosis and also for brain lesion diagnosis and monitoring. Methods: The developed CAD system includes different user-friendly tools for segmenting and determining whole brain and brain structure volumes in an easy and accurate way. Specifically, three types of measurements can be performed: (1) total volume of white, gray matter and cerebrospinal fluid; (2) brain structure volumes (volume of putamen, thalamus, hippocampus and caudate nucleus); and (3) brain lesion volumes. Results: As a proof of concept, some study cases were analyzed with the presented system achieving promising results. In addition to be used to quantify treatment effectiveness in patients with brain lesions, it was demonstrated that BRAIM is able to classify a subject according to the brain volume measurements using as reference a healthy control database created for this purpose. Conclusions: The CAD system presented in this paper simplifies the daily work of clinicians and provides them with objective and quantitative volume data for prospective and retrospective analyses. (C) 2017 Elsevier B.V. All rights reserved.This work has been supported by the Centro para el Desarrollo Tecnologico Industrial (CDTI) under the project BRAIM (IDI-20130020)Morales, S.; Bernabeu-Sanz, A.; López-Mir, F.; Gonzalez, P.; Luna, L.; Naranjo Ornedo, V. (2017). BRAIM: A computer-aided diagnosis system for neurodegenerative diseases and brain lesion monitoring from volumetric analyses. Computer Methods and Programs in Biomedicine. 145:167-179. https://doi.org/10.1016/j.cmpb.2017.04.006S16717914

3D segmentation of glioma from brain MR images using seeded region growing and fuzzy c-means clustering

This thesis presents two algorithms for brain MR image segmentation. The images used are axial MR images of the human brain. The images show a glioma. The objective is to segment the tumour and edema surrounding it from the images. Initially the images are pre-processed by contrast adjustment. Segmentation is performed by two algorithms: seeded region growing and fuzzy c-means clustering. After the images are segmented, the volumes of the segmented regions are measured. The segmentation is done in MATLAB. Finally the results are rendered in 3D in AMIRA

Biomechanics of Traumatic Brain Injury

This project is aimed to generate a 2D model of head to study biomechanics of head injuries due to external forces acting in different direction. The purpose of this work is the development of a 2D finite element model (FEM), using equations of elasticity and viscoelasticity to model the stress-strain distribution in the head due to external impacts. A variational constitutive model for soft biological tissues such as brain is utilized to reproduce axonal damage and cavitation injury through inelastic deformation and with the constitutive model for hard tissues such as brain possessing elastic properties. A constitutive model for these biological tissues is formulated with a finite strain regime. Most of the physiological damage to living tissues are caused by relative motions within the tissues (e.g. in head injury, due to relative motion between brain and skull), due to tensile and shearing structural failures. The Model includes skull, brain and CSF as major components so material response is split into elastic and viscoelastic components, including rate effects, shear and porous plasticity and finite viscoelasticity. To describe biological soft tissues such as brain tissue a viscoelastic material model is employed and to describe skull and cerebrospinal fluid we are an elastic model is employed. Skull is considered to be transverse isotropic. The present FEM simulation focuses on brain injuries from static and dynamic loading resulting from frontal, top, back and oblique head impacts and prediction of localization, extension, and intensity of tissue damage. In the present work, brain 2D geometry is generated from MRI of adult head. We intend to obtain insight into the severity of brain injury by modeling by analyzing the stress-strain pattern under static and dynamic loading

Segmentation d'images IRM du cerveau pour la construction d'un modèle anatomique destiné à la simulation bio-mécanique

Comment obtenir des données anatomiques pendant une neurochirurgie ? a été ce qui a guidé le travail développé dans le cadre de cette thèse. Les IRM sont actuellement utilisées en amont de l'opération pour fournir cette information, que ce soit pour le diagnostique ou pour définir le plan de traitement. De même, ces images pre-opératoires peuvent aussi être utilisées pendant l'opération, pour pallier la difficulté et le coût des images per-opératoires. Pour les rendre utilisables en salle d'opération, un recalage doit être effectué avec la position du patient. Cependant, le cerveau subit des déformations pendant la chirurgie, phénomène appelé Brain Shift, ce qui altère la qualité du recalage. Pour corriger cela, d'autres données per-opératoires peuvent être acquises, comme la localisation de la surface corticale, ou encore des images US localisées en 3D. Ce nouveau recalage permet de compenser ce problème, mais en partie seulement. Ainsi, des modèles mécaniques ont été développés, entre autres pour apporter des solutions à l'amélioration de ce recalage. Ils permettent ainsi d'estimer les déformations du cerveau. De nombreuses méthodes existent pour implémenter ces modèles, selon différentes lois de comportement et différents paramètres physiologiques. Dans tous les cas, cela requiert un modèle anatomique patient-spécifique. Actuellement, ce modèle est obtenu par contourage manuel, ou quelquefois semi-manuel. Le but de ce travail de thèse est donc de proposer une méthode automatique pour obtenir un modèle du cerveau adapté sur l'anatomie du patient, et utilisable pour une simulation mécanique. La méthode implémentée se base sur les modèles déformables pour segmenter les structures anatomiques les plus pertinentes dans une modélisation bio-mécanique. En effet, les membranes internes du cerveau sont intégrées: falx cerebri and tentorium cerebelli. Et bien qu'il ait été démontré que ces structures jouent un rôle primordial, peu d'études les prennent en compte. Par ailleurs, la segmentation résultante de notre travail est validée par comparaison avec des données disponibles en ligne. De plus, nous construisons un modèle 3D, dont les déformations seront simulées en utilisant une méthode de résolution par Éléments Finis. Ainsi, nous vérifions par des expériences l'importance des membranes, ainsi que celle des paramètres physiologiques.The general problem that motivates the work developed in this thesis is: how to obtain anatomical information during a neurosurgery?. Magnetic Resonance (MR) images are usually acquired before the surgery to provide anatomical information for diagnosis and planning. Also, the same images are commonly used during the surgery, because to acquire MRI images in the operating room is complex and expensive. To make these images useful inside the operating room, a registration between them and the patient's position has to be processed. The problem is that the brain suffers deformations during the surgery, in a process called brain shift, degrading the quality of registration. To correct this, intra-operative information may be used, for example, the position of the brain surface or US images localized in 3D. The new registration will compensate this problem, but only to a certain extent. Mechanical models of the brain have been developed as a solution to improve this registration. They allow to estimate brain deformation under certain boundary conditions. In the literature, there are a variety of methods for implementing these models, different equation laws used for continuum mechanic, and different reported mechanical properties of the tissues. However, a patient specific anatomical model is always required. Currently, most mechanical models obtain the associated anatomical model by manual or semi-manual segmentation. The aim of this thesis is to propose and implement an automatic method to obtain a model of the brain fitted to the patient's anatomy and suitable for mechanical modeling. The implemented method uses deformable model techniques to segment the most relevant anatomical structures for mechanical modeling. Indeed, the internal membranes of the brain are included: falx cerebri and tentorium cerebelli. Even though the importance of these structures is stated in the literature, only a few of publications include them in the model. The segmentation obtained by our method is assessed using the most used online databases. In addition, a 3D model is constructed to validate the usability of the anatomical model in a Finite Element Method (FEM). And the importance of the internal membranes and the variation of the mechanical parameters is studied.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Intracranial fluids dynamics: a quantitative evaluation by means of phase-contrast magnetic resonance imaging

El volumen intracraneal lo integran el volumen de líquido cefalorraquídeo (LCR), el de la sangre y el del parénquima cerebral. La entrada de sangre al cráneo en la sístole incrementa el volumen intracraneal. Según la ley de Monroe-Kellie debe ocurrir una descompensación en los volúmenes restantes para mantener constante el volumen total. Los desequilibrios que se producen en este proceso de la homeostasis cerebral se han asociado tanto a enfermedades neurodegenerativas como a cerebrovasculares. Por tanto, es necesario contar con metodologías adecuadas para analizar la dinámica de los fluidos intracraneales (LCR y sangre). Las secuencias dinámicas de resonancia magnética en contraste de fase (RM-CF) con sincronismo cardíaco permiten cuantificar el flujo de LCR y de sangre durante un ciclo cardíaco. La medición de flujo mediante secuencias de RM-CF es precisa y reproducible siempre que se use un protocolo de adquisición adecuado. La reproducibilidad y exactitud de las medidas dependen también del uso de técnicas adecuadas de posproceso que permitan segmentar las regiones de interés (ROI) independientemente del operador y admitan corregir los errores de fondo introducidos por la supresión imperfecta de las corrientes inducidas y la contribución a la señal de los pequeños movimientos que presenta el mesencéfalo por la transmisión del pulso vascular así como el submuestreo (aliasing), reflejado como un cambio abrupto y opuesto del sentido original del flujo. Estas técnicas de análisis deben también tener en cuenta los errores relacionados con el efecto de volumen parcial (EVP), causado por la presencia de tejido estacionario y de flujo en el interior de los vóxeles de la periferia de la región a estudiar El objetivo principal de esta tesis es desarrollar una metodología reproducible para evaluar cuantitativamente la dinámica de los fluidos intracraneales dentro de espacios de LCR (acueducto de Silvio, cisterna prepontina y espacio perimedular C2C3) y principales vaFlórez Ordóñez, YN. (2009). Intracranial fluids dynamics: a quantitative evaluation by means of phase-contrast magnetic resonance imaging [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/6029Palanci

Employing Symmetry Features for Automatic Misalignment Correction in Neuroimages

A novel method to automatically compute the symmetry plane and to correct the 3D orientation of neuro-images is presented. In acquisition of neuroimaging scans, the lack of perfect alignment of a patient's head makes it challenging to evaluate brain images. By deploying a shape-based criterion, the symmetry plane is defined as a plane that best matches external surface points on one side of the head, with their counterparts on the other side. In our method, the head volume is represented as a re-parameterized surface point cloud, where each location is parameterized by its elevation (latitude), azimuth (longitude), and radius. The search for the best matching surfaces is implemented in a multi-resolution paradigm, and the computation time is significantly decreased. The algorithm was quantitatively evaluated using in both simulated data and in real T1, T2, Flair magnetic resonance patient images. This algorithm is found to be fast (< 10s per MR volume), robust and accurate (< .6 degree of Mean Angular Error), invariant to the acquisition noise, slice thickness, bias field, and pathological asymmetries

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2D/3D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on 8 publicly available datasets (six 2D datasets, one 3D dataset and one 3D synthetic dataset) demonstrate its superior performance to other state-ofthe- art methods