25 research outputs found

    Brachial plexus injury mimicking a spinal-cord injury.

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    Objective High-energy impact to the head, neck, and shoulder can result in cervical spine as well as brachial plexus injuries. Because cervical spine injuries are more common, this tends to be the initial focus for management. We present a case in which the initial magnetic resonance imaging (MRI) was somewhat misleading and a detailed neurological exam lead to the correct diagnosis.Clinical presentation A 19-year-old man presented to the hospital following a shoulder injury during football practice. The patient immediately complained of significant pain in his neck, shoulder, and right arm and the inability to move his right arm. He was stabilized in the field for a presumed cervical-spine injury and transported to the emergency department.Intervention Initial radiographic assessment (C-spine CT, right shoulder x-ray) showed no bony abnormality. MRI of the cervical-spine showed T2 signal change and cord swelling thought to be consistent with a cord contusion. With adequate pain control, a detailed neurological examination was possible and was consistent with an upper brachial plexus avulsion injury that was confirmed by CT myelogram. The patient failed to make significant neurological recovery and he underwent spinal accessory nerve grafting to the suprascapular nerve to restore shoulder abduction and external rotation, while the phrenic nerve was grafted to the musculocutaneous nerve to restore elbow flexion.Conclusion Cervical spinal-cord injuries and brachial plexus injuries can occur by the same high energy mechanisms and can occur simultaneously. As in this case, MRI findings can be misleading and a detailed physical examination is the key to diagnosis. However, this can be difficult in polytrauma patients with upper extremity injuries, head injuries or concomitant spinal-cord injury. Finally, prompt diagnosis and early surgical renerveration have been associated with better long-term recovery with certain types of injury

    Patient With Severe Moyamoya Disease Who Presents With Acute Cortical Blindness

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    Cell Counting and Segmentation of Immunohistochemical Images in the Spinal Cord: Comparing Deep Learning and Traditional Approaches

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    © 2018 IEEE. Estimation of cell nuclei in images stained for the c-fos protein using immunohistochemistry (IHC) is infeasible in large image sets. Use of multiple human raters to increase throughput often creates variance in the data analysis. Machine learning techniques for biomedical image analysis have been explored for cell-counting in pathology, but their performance on IHC staining, especially to label activated cells in the spinal cord is unknown. In this study, we evaluate different machine learning techniques to segment and count spinal cord neurons that have been active during stepping. We present a qualitative as well as quantitative comparison of algorithmic performance versus two human raters. Quantitative ratings are presented with cell-count statistics and Dice (DSI) scores. We also show the degree of variability between multiple human raters' segmentations and observe that there is a higher degree of variability in segmentations produced by classic machine learning techniques (SVM and Random forest) as compared to the newer deep learning techniques. The work presented here, represents the first steps towards addressing the analysis time bottleneck of large image data sets generated by c-fos IHC staining techniques, a task that would be impossible to do manually

    Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification

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    Diagnosis and timely treatment play an important role in preventing brain tumor growth. Deep learning methods have gained much attention lately. Obtaining a large amount of annotated medical data remains a challenging issue. Furthermore, high dimensional features of brain images could lead to over-fitting. In this paper, we address the above issues. Firstly, we propose an architecture for Generative Adversarial Networks to generate good quality synthetic 2D MRIs from multi-modality MRIs (T1 contrast-enhanced, T2, FLAIR). Secondly, we propose a deep learning scheme based on 3-streams of Convolutional Autoencoders (CAEs) followed by sensor information fusion. The rational behind using CAEs is that it may improve glioma classification performance (as comparing with conventional CNNs), since CAEs offer noise robustness and also efficient feature reduction hence possibly reduce the over-fitting. A two-round training strategy is also applied by pre-training on GAN augmented synthetic MRIs followed by refined-training on original MRIs. Experiments on BraTS 2017 dataset have demonstrated the effectiveness of the proposed scheme (test accuracy 92.04%). Comparison with several exiting schemes has provided further support to the proposed scheme
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