972 research outputs found
Policy issues in interconnecting networks
To support the activities of the Federal Research Coordinating Committee (FRICC) in creating an interconnected set of networks to serve the research community, two workshops were held to address the technical support of policy issues that arise when interconnecting such networks. The workshops addressed the required and feasible technologies and architectures that could be used to satisfy the desired policies for interconnection. The results of the workshop are documented
Telescience testbed pilot program
The Universities Space Research Association (USRA), under sponsorship from the NASA Office of Space Science and Applications, is conducting a Telescience Testbed Pilot Program. Fifteen universities, under subcontract to USRA, are conducting a variety of scientific experiments using advanced technology to determine the requirements and evaluate the tradeoffs for the information system of the Space Station era. An interim set of recommendations based on the experiences of the first six months of the pilot program is presented
Telescience Testbed Pilot Program
The Telescience Testbed Pilot Program is developing initial recommendations for requirements and design approaches for the information systems of the Space Station era. During this quarter, drafting of the final reports of the various participants was initiated. Several drafts are included in this report as the University technical reports
Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
Segmentation of the heart in cardiac cine MR is clinically used to quantify
cardiac function. We propose a fully automatic method for segmentation and
disease classification using cardiac cine MR images. A convolutional neural
network (CNN) was designed to simultaneously segment the left ventricle (LV),
right ventricle (RV) and myocardium in end-diastole (ED) and end-systole (ES)
images. Features derived from the obtained segmentations were used in a Random
Forest classifier to label patients as suffering from dilated cardiomyopathy,
hypertrophic cardiomyopathy, heart failure following myocardial infarction,
right ventricular abnormality, or no cardiac disease. The method was developed
and evaluated using a balanced dataset containing images of 100 patients, which
was provided in the MICCAI 2017 automated cardiac diagnosis challenge (ACDC).
The segmentation and classification pipeline were evaluated in a four-fold
stratified cross-validation. Average Dice scores between reference and
automatically obtained segmentations were 0.94, 0.88 and 0.87 for the LV, RV
and myocardium. The classifier assigned 91% of patients to the correct disease
category. Segmentation and disease classification took 5 s per patient. The
results of our study suggest that image-based diagnosis using cine MR cardiac
scans can be performed automatically with high accuracy.Comment: Accepted in STACOM Automated Cardiac Diagnosis Challenge 201
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