Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Analysis of Growing Tumor on the Flow Velocity of Cerebrospinal Fluid in Human Brain Using Computational Modeling and Fluid-Structure Interaction

Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of Brain. Intracranial compartments such as blood, brain and CSF are incompressible in nature. Therefore, if a volume imbalance in one of the aforenoted compartments is observed, the other reaches out to maintain net change to zero. Whereas, CSF has higher compliance over long term. However, if the CSF flow is obstructed in the ventricles, this compliance may get exhausted early. Brain tumor on the other hand poses a similar challenge towards destabilization of CSF flow by compressing any section of ventricles thereby ensuing obstruction. To avoid invasive procedures to study effects of tumor on CSF flow, numerical-based methods such as Finite element modeling (FEM) are used which provide excellent description of underlying pathological interaction. A 3D fluid-structure interaction (FSI) model is developed to study the effect of tumor growth on the flow of cerebrospinal fluid in ventricle system. The FSI model encapsulates all the physiological parameters which may be necessary in analyzing intraventricular CSF flow behavior. Findings of the model show that brain tumor affects CSF flow parameters by deforming the walls of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow velocity and considerable deformation on the walls of ventricles

Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Processing Capability

The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy

AUTOMATIC 3D DEFORMED MIDSAGITTAL SURFACE LOCALIZATION BY CONSTRAINED MONTE CARLO OPTIMIZATION

AUTOMATIC 3D DEFORMED MIDSAGITTAL SURFACE LOCALIZATION BY CONSTRAINED MONTE CARLO OPTIMIZATIO

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

Towards a data-driven treatment of epilepsy: computational methods to overcome low-data regimes in clinical settings

Epilepsy is the most common neurological disorder, affecting around 1 % of the population. One third of patients with epilepsy are drug-resistant. If the epileptogenic zone can be localized precisely, curative resective surgery may be performed. However, only 40 to 70 % of patients remain seizure-free after surgery. Presurgical evaluation, which in part aims to localize the epileptogenic zone (EZ), is a complex multimodal process that requires subjective clinical decisions, often relying on a multidisciplinary team’s experience. Thus, the clinical pathway could benefit from data-driven methods for clinical decision support. In the last decade, deep learning has seen great advancements due to the improvement of graphics processing units (GPUs), the development of new algorithms and the large amounts of generated data that become available for training. However, using deep learning in clinical settings is challenging as large datasets are rare due to privacy concerns and expensive annotation processes. Methods to overcome the lack of data are especially important in the context of presurgical evaluation of epilepsy, as only a small proportion of patients with epilepsy end up undergoing surgery, which limits the availability of data to learn from. This thesis introduces computational methods that pave the way towards integrating data-driven methods into the clinical pathway for the treatment of epilepsy, overcoming the challenge presented by the relatively small datasets available. We used transfer learning from general-domain human action recognition to characterize epileptic seizures from video–telemetry data. We developed a software framework to predict the location of the epileptogenic zone given seizure semiologies, based on retrospective information from the literature. We trained deep learning models using self-supervised and semi-supervised learning to perform quantitative analysis of resective surgery by segmenting resection cavities on brain magnetic resonance images (MRIs). Throughout our work, we shared datasets and software tools that will accelerate research in medical image computing, particularly in the field of epilepsy

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

A Robust Grey Wolf-based Deep Learning for Brain Tumour Detection in MR Images

In recent times, the detection of brain tumour is a common fatality in the field of the health community. Generally, the brain tumor is an abnormal mass of tissue where the cells grow up and increase uncontrollably, apparently unregulated by mechanisms that control cells. A number of techniques have been developed so far; however, the time consumption in detecting brain tumor is still a challenge in the field of image processing.  This paper intends to propose a new detection model even accurately. The model includes certain processes like Preprocessing, Segmentation, Feature Extraction and Classification. Particularly, two extreme processes like contrast enhancement and skull stripping are processed under initial phase, in the segmentation process, this paper uses Fuzzy Means Clustering (FCM) algorithm. Both Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM) features are extracted in feature extraction phase. Moreover, this paper uses Deep Belief Network (DBN) for classification. The DBN is integrated with the optimization approach, and hence this paper introduces the optimized DBN, for which Grey Wolf Optimization (GWO) is used here.  The proposed model is termed as GW-DBN model. The proposed model compares its performance over other conventional methods in terms of Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F1Score and Matthews Correlation Coefficient (MCC), False negative rate (FNR), False positive rate (FPR) and False Discovery Rate (FDR), and proven the superiority of proposed work.

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction☆

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome