Intracranial Hypertension, a disorder characterized by elevated pressure in the brain, is typically monitored in neurointensive care and diagnosed only after elevation has occurred. This reaction-based method of treatment leaves patients at higher risk of additional complications in case of misdetection. The detection of intracranial hypertension has been the subject of many recent studies in an attempt to accurately characterize the causes of hypertension, specifically examining waveform morphology. We investigate the use of Deep Learning, a hierarchical form of machine learning, to model the relationship between hypertension and waveform morphology, giving us the ability to accurately detect presence hypertension. Data from 60 patients, showing intracranial pressure levels over a half hour time span, was used to evaluate the model. We divided each patient’s recording into average normalized beats over 30 sec segments, assigning each beat a label of high (i.e. greater than 15 mmHg) or low intracranial pressure. The model was tested to predict the presence of elevated intracranial pressure. The algorithm was found to be 92.05± 2.25% accurate in detecting intracranial hypertension on our dataset.

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New tissue-clearing techniques and improvements in optical microscopy have rapidly advanced capabilities to acquire volumetric imagery of neural tissue at resolutions of one micron or better. As sizes for data collections increase, accurate automatic segmentation of cell nuclei becomes increasingly important for quantitative analysis of imaged tissue. We present a cell nucleus segmentation method that is formulated as a parameter estimation problem with the goal of determining the count, shapes, and locations of nuclei that most accurately describe an image. We applied our new voting-based approach to fluorescence confocal microscopy images of neural tissue stained with DAPI, which highlights nuclei. Compared to manual counting of cells in three DAPI images, our method outperformed three existing approaches. On a manually labeled highresolution DAPI image, our method also outperformed those methods and achieved a cell count accuracy of 98.99% and mean Dice coefficient of 0.6498.

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Background: An automated, unbiased method to accurately label cerebrovascular territories would greatly advance our ability to assess individual stroke patients as well as study large databases. Previous attempts have failed due to the wide variation in normal vascular topography. We test the hypothesis that a nonparametric probabilistic model that learns the configurational characteristics of vascular territories will better annotate the cerebrovasculature. Methods: In the George Mason Brain Vasculature database, we identified patients with MRA reconstructions segmented into seven major regions (left and right MCA, PCA, and ACA and Circle of Willis). We then augment these labels by manually segmenting the MCA territory into an additional eight regions. We then divide the database into training and validation cohorts to assess the algorithm. Results: Among 54 patients that met the inclusion criteria, 39 reconstructions were used as training input to the model among the 61 digital reconstructions of human brain arterial structures available. The model was then validated on an independent cohort of 15 patients. The algorithm was found to be 94+-5.2% accurate in annotating the vascular segments. Conclusions: Kernel density estimation, used in conjunction with a Bayesian inference-based algorithm, can accurately label cerebral vascular territories using spatial and radial features. This process can provide a framework for further vascular segmentation and analysis of artery occlusion in stroke patients.