TBI Contusion Segmentation from MRI using Convolutional Neural Networks

Traumatic brain injury (TBI) is caused by a sudden trauma to the head that may result in hematomas and contusions and can lead to stroke or chronic disability. An accurate quantification of the lesion volumes and their locations is essential to understand the pathophysiology of TBI and its progression. In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of patients with TBI. The CNN architecture proposed here was based on a state of the art CNN architecture from Google, called Inception. Using a 3-layer Inception network, lesions are segmented from multi-contrast MR images. When compared with two recent TBI lesion segmentation methods, one based on CNN (called DeepMedic) and another based on random forests, the proposed algorithm showed improved segmentation accuracy on images of 18 patients with mild to severe TBI. Using a leave-one-out cross validation, the proposed model achieved a median Dice of 0.75, which was significantly better (p<0.01) than the two competing methods.Comment: https://ieeexplore.ieee.org/abstract/document/8363545/, IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018

Political participation under conditions of (democratic) duress

© The Author(s) 2019. Drawing on Amartya Sen’s concept of agency and capability, this article explores political participation in three dimensions: individual dispositions, opportunities for participation, and processes of participation. It presents an analytical approach that examines these dimensions in relation to practices of participation as interactions between the State and citizens within and outside of political institutions. Two examples are used to illustrate the utility of this approach in states where democratic institutions are deficient. The first example historically traces the evolution of tribal informal institutions in Jordan to demonstrate how and why they mediate people’s participation in the public sphere. The second example uses narrative inquiry to explore community activists’ aspiration for and commitment to political expression through social media in Vietnam. Both examples suggest that a country’s political institutions and its rule of law may shape political cultures and societal values of participation, but it is the individuals’ recognition and response to these structures that ultimately create their motivations and the opportunities for them to participate. The article emphasises the importance of understanding the contexts in which the respective tradition of political participation takes place in order to understand the outcomes as well as the conditions for participation, especially in contexts that theoretically qualify as authoritarian

New Era, New Opportunity, Is GES DISC Ready for Big Data Challenge?

The new era of Big Data has opened doors for many new opportunities, as well as new challenges, for both Earth science research/application and data communities. As one of the twelve NASA data centers - Goddard Earth Sciences Data and Information Services Center (GES DISC), one of our great challenges has been how to help research/application community efficiently (quickly and properly) accessing, visualizing and analyzing the massive and diverse data in natural hazard research, management, or even prediction. GES DISC has archived over 2000 TB data on premises and distributed over 23,000 TB of data since 2010. Our data has been widely used in every phase of natural hazard management and research, i.e. long term risk assessment and reduction, forecasting and predicting, monitoring and detection, early warning, damage assessment and response. The big data challenge is not just about data storage, but also about data discoverability and accessibility, and even more, about data migration/mirroring in the cloud. This paper is going to demonstrate GES DISCs efforts and approaches of evolving our overall Web services and powerful Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) tool into further improving data discoverability and accessibility. Prototype works will also be presented

Coherence of Nitrogen-Vacancy Electronic Spin Ensembles in Diamond

We present an experimental and theoretical study of electronic spin decoherence in ensembles of nitrogen-vacancy (NV) color centers in bulk high-purity diamond at room temperature. Under appropriate conditions, we find ensemble NV spin coherence times (T_2) comparable to that of single NVs, with T_2 > 600 microseconds for a sample with natural abundance of 13C and paramagnetic impurity density ~10^15 cm^(-3). We also observe a sharp decrease of the coherence time with misalignment of the static magnetic field relative to the NV electronic spin axis, consistent with theoretical modeling of NV coupling to a 13C nuclear spin bath. The long coherence times and increased signal-to-noise provided by room-temperature NV ensembles will aid many applications of NV centers in precision magnetometry and quantum information.Comment: 5 pages, 3 figures; v2 minor correction

A Novel Lumbar Motion Segment Classification to Predict Changes in Segmental Sagittal Alignment After Lateral Interbody Fixation.

Study designRetrospective cohort study.ObjectivesLateral interbody fixation is being increasingly used for the correction of segmental sagittal parameters. One factor that affects postoperative correction is the resistance afforded by posterior hypertrophic facet joints in the degenerative lumbar spine. In this article, we describe a novel preoperative motion segment classification system to predict postoperative correction of segmental sagittal alignment after lateral lumbar interbody fusion.MethodsPreoperative computed tomography scans were analyzed for segmental facet osseous anatomy for all patients undergoing lateral lumbar interbody fusion at 3 institutions. Each facet was assigned a facet grade (min = 0, max = 2), and the sum of the bilateral facet grades was the final motion segment grade (MSG; min = 0, max = 4). Preoperative and postoperative segmental lordosis was measured on standing lateral radiographs. Postoperative segmental lordosis was also conveyed as a percentage of the implanted graft lordosis (%GL). Simple linear regression was conducted to predict the postoperative segmental %GL according to MSG.ResultsA total of 36 patients with 59 operated levels were identified. There were 19 levels with MSG 0, 14 levels with MSG 1, 13 levels with MSG 2, 8 levels with MSG 3, and 5 levels with MSG 4. Mean %GL was 115%, 90%, 77%, 43%, and 5% for MSG 0 to 4, respectively. MSG significantly predicted postoperative %GL (P &lt; .01). Each increase in MSG was associated with a 28% decrease in %GL.ConclusionsWe propose a novel facet-based motion segment classification system that significantly predicted postoperative segmental lordosis after lateral lumbar interbody fusion