Tree of Life Synagogue Shooting in Pittsburgh: Preparedness, Prehospital Care, and Lessons Learned

On Saturday, October 27, 2018, a man with anti-Semitic motivations entered Tree of Life synagogue in the Squirrel Hill section of Pittsburgh, Pennsylvania; he had an AR-15 semi-automatic rifle and three handguns, opening fire upon worshippers. Eventually 11 civilians died at the scene and eight people sustained non-fatal injuries, including five police officers. Each person injured but alive at the scene received care at one of three local level-one trauma centers.  The injured had wounds often seen in war-settings, with the signature of high velocity weaponry.  We describe the scene response, specific elements of our hospital plans, the overall out-of-hospital preparedness in Pittsburgh, and the lessons learned

From Short-term Hotspot Measurements to Long-term Module Reliability

AbstractIn order to reach high module reliability, all solar cells with a potentially critical hotspot have to be neglected during cell sorting. This is essential to avoid delamination in case of partial shading of the module. Due to throughput considerations, the finished solar cell has to be classified within some milliseconds. In consequence the short-term hotspot heating measurement has to be correlated to absolute hotspot temperatures for various module conditions in the field. Previously it has already been shown that a definite mapping of these quantities is not possible, requiring further investigations in order to quantify the risk for possible module damage.In this contribution, the probability distribution for absolute hotspot temperatures in the module will be calculated from short-term hotspot measurement data, considering temperature-dependent reverse biases. Together with experimental data for module delamination temperatures, the probability of module failure can be calculated in a direct way

Deep convolutional networks for automated detection of posterior-element fractures on spine CT

Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of 'positive', i.e. fractured posterior-elements and 'negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.Comment: To be presented at SPIE Medical Imaging, 2016, San Dieg

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits previous segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans, using multi-level deep convolutional networks (ConvNets). We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. We first present a dense labeling of local image patches via $P{-}\mathrm{ConvNet}$ and nearest neighbor fusion. Then we describe a regional ConvNet ($R_1{-}\mathrm{ConvNet}$) that samples a set of bounding boxes around each image superpixel at different scales of contexts in a "zoom-out" fashion. Our ConvNets learn to assign class probabilities for each superpixel region of being pancreas. Last, we study a stacked $R_2{-}\mathrm{ConvNet}$ leveraging the joint space of CT intensities and the $P{-}\mathrm{ConvNet}$ dense probability maps. Both 3D Gaussian smoothing and 2D conditional random fields are exploited as structured predictions for post-processing. We evaluate on CT images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity Coefficient of 83.6$\pm$6.3% in training and 71.8$\pm$10.7% in testing.Comment: To be presented at MICCAI 2015 - 18th International Conference on Medical Computing and Computer Assisted Interventions, Munich, German