A brief report on MRI investigation of experimental traumatic brain injury

Traumatic brain injury is a major cause of death and disability. This is a brief report based on a symposium presentation to the 2014 Chinese Neurotrauma Association Meeting in San Francisco, USA. It covers the work from our laboratory in applying multimodal MRI to study experimental traumatic brain injury in rats with comparisons made to behavioral tests and histology. MRI protocols include structural, perfusion, manganese-enhanced, diffusion-tensor MRI, and MRI of blood-brain barrier integrity and cerebrovascular reactivity.

Modified Visual Geometric Group Architecture for MRI Brain Image Classification

The advancement of automated medical diagnosis in biomedical engineering has become an important area of research.Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences.The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal.The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models.One of the best models for image localization and classification is the Visual Geometric Group(VGG)model.In this study,an efficient modified VGG architecture for brain image classification is developed using transfer learning.The pooling layer is modified to enhance the classification capability of VGG architecture.Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5%improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa(REMBRANDT)database.

SC-Net:A New U-Net Network for Hippocampus Segmentation

Neurological disorders like Alzheimer’s disease have a significant impact on the lives and health of the elderly as the aging population con-tinues to grow.Doctors can achieve effective prevention and treatment of Alzheimer’s disease according to the morphological volume of hippocam-pus.General segmentation techniques frequently fail to produce satisfactory results due to hippocampus’s small size,complex structure,and fuzzy edges.We develop a new SC-Net model using complete brain MRI images to achieve high-precision segmentation of hippocampal structures.The proposed network improves the accuracy of hippocampal structural segmentation by retaining the original location information of the hippocampus.Extensive experimental results demonstrate that the proposed SC-Net model is signif-icantly better than other models,and reaches a Dice similarity coefficient of 0.885 on Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset.

A Survey of MRI-Based Brain Tumor Segmentation Methods

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core,and edema from normal brain tissues of White Matter（WM）, Gray Matter（GM）, and Cerebrospinal Fluid（CSF）. MRIbased brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging（MRI） images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.