Dynamic low-level context for the detection of mild traumatic brain injury.

Mild traumatic brain injury (mTBI) appears as low contrast lesions in magnetic resonance (MR) imaging. Standard automated detection approaches cannot detect the subtle changes caused by the lesions. The use of context has become integral for the detection of low contrast objects in images. Context is any information that can be used for object detection but is not directly due to the physical appearance of an object in an image. In this paper, new low-level static and dynamic context features are proposed and integrated into a discriminative voxel-level classifier to improve the detection of mTBI lesions. Visual features, including multiple texture measures, are used to give an initial estimate of a lesion. From the initial estimate novel proximity and directional distance, contextual features are calculated and used as features for another classifier. This feature takes advantage of spatial information given by the initial lesion estimate using only the visual features. Dynamic context is captured by the proposed posterior marginal edge distance context feature, which measures the distance from a hard estimate of the lesion at a previous time point. The approach is validated on a temporal mTBI rat model dataset and shown to have improved dice score and convergence compared to other state-of-the-art approaches. Analysis of feature importance and versatility of the approach on other datasets are also provided

Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury.

Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care

Differential detection of impact site versus rotational site injury by magnetic resonance imaging and microglial morphology in an unrestrained mild closed head injury model.

Seventy-five percent of all traumatic brain injuries are mild and do not cause readily visible abnormalities on routine medical imaging making it difficult to predict which individuals will develop unwanted clinical sequelae. Microglia are brain-resident macrophages and early responders to brain insults. Their activation is associated with changes in morphology or expression of phenotypic markers including P2Y12 and major histocompatibility complex class II. Using a murine model of unrestrained mild closed head injury (mCHI), we used microglia as reporters of acute brain injury at sites of impact versus sites experiencing rotational stress 24 h post-mCHI. Consistent with mild injury, a modest 20% reduction in P2Y12 expression was detected by quantitative real-time PCR (qPCR) analysis but only in the impacted region of the cortex. Furthermore, neither an influx of blood-derived immune cells nor changes in microglial expression of CD45, TREM1, TREM2, major histocompatibility complex class II or CD40 were detected. Using magnetic resonance imaging (MRI), small reductions in T2 weighted values were observed but only near the area of impact and without overt tissue damage (blood deposition, edema). Microglial morphology was quantified without cryosectioning artifacts using ScaleA(2) clarified brains from CX3CR1-green fluorescence protein (GFP) mice. The cortex rostral to the mCHI impact site receives greater rotational stress but neither MRI nor molecular markers of microglial activation showed significant changes from shams in this region. However, microglia in this rostral region did display signs of morphologic activation equivalent to that observed in severe CHI. Thus, mCHI-triggered rotational stress is sufficient to cause injuries undetectable by routine MRI that could result in altered microglial surveillance of brain homeostasis. Acute changes in microglial morphology reveal brain responses to unrestrained mild traumatic brain injury In areas subjected to rotational stress distant from impact site In the absence of detectable changes in standard molecular indicators of brain damage, inflammation or microglial activation. That might result in decreased surveillance of brain function and increased susceptibility to subsequent brain insults

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

Detection of Brain Injury Using Different Soft Computing Techniques: A Survey

The detection of brain injury is one of the important and difficult task in the field of medicine. If the brain injuries are not detected in time, then it can cause serious problems in patients and sometimes can even lead to death. Traumatic brain injury (TBI) is one of the major causes of mortality and poor quality of life among the survivors. Various imaging techniques are available for taking the images of the brain so that the injuries can be detected. Magnetic resonance imaging (MRI) is one of the common medical imaging technique used for the delineation of soft tissues such as that of the brain. This paper analyses few of the methods and their performances that have been proposed for the detection of the brain injury. In these methods different soft computing techniques such as artificial neural networks (ANN), k nearest neighbor (k-NN), support vector machine (SVM), Parzan window, etc. were used for the classification of abnormal and normal brain images. Before classification feature extraction and reduction were done using the methods such as DWT, GLCM, PCA, etc. DOI: 10.17762/ijritcc2321-8169.15030

A Review on Detection of Traumatic brain Injury using Visual-Contextual model in MRI Images

Recently, there are various computational methods to analyze the traumatic brain injury (TBI) from magnetic resonance imaging (MRI).The detection of brain injury is very difficult task in the medical science. There are various soft techniques for the detection of the patch of brain injury on the basis of MRI image contents. This paper gives brief analysis about the different methods to determine the normal and abnormal tissues of the brain