Each year, about 500 natural disasters kill approximately 70,000 people and affect more than 200 million people worldwide. In the aftermath of such events, large quantities of supplies are needed to provide relief aid to the affected. CARE International is one of the largest humanitarian organizations that provide relief aid to disaster survivors. The most vital issues in disaster response are agility in mobilizing supplies and effectiveness in distributing them. To improve disaster response, a research group from Georgia Institute of Technology collaborated with CARE to develop a model to evaluate the effect that pre-positioning relief items would have on CARE's average relief-aid emergency response time. The model's results helped CARE managers to determine a desired configuration for the organization's pre-positioning network. Based on the results of our study and other factors, CARE has pre-positioned relief supplies in three facilities around the world.

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IV.

Introduction: Intravascular optical coherence tomography (IVOCT) is an in-vivo imaging modality based on the introduction of a catheter in a blood vessel for viewing its inner wall using electromagnetic radiation. One of the most developed automatic applications for this modality is the lumen area segmentation, however on the evaluation of these methods, the slices inside bifurcation regions, or with the presence of complex atherosclerotic plaques and dissections are usually discarded. This paper describes a fully-automatic method for computing the lumen area in IVOCT images where the set of slices includes complex atherosclerotic plaques and dissections. Methods: The proposed lumen segmentation method is divided into two steps: preprocessing, including the removal of artifacts and the second step comprises a lumen detection using morphological operations. In addition, it is proposed an approach to delimit the lumen area for slices inside bifurcation region, considering only the main branch. Results: Evaluation of the automatic lumen segmentation used manual segmentations as a reference, it was performed on 1328 human IVOCT images, presenting a mean difference in lumen area and Dice metrics of 0.19 mm 2 and 97% for slices outside the bifurcation, 1.2 mm 2 and 88% in the regions with bifurcation without automatic contour correction and 0.52 mm 2 and 90% inside bifurcation region with automatic contour correction. Conclusion: This present study shows a robust lumen segmentation method for vessel cross-sections with dissections and complex plaque and bifurcation avoiding the exclusion of such regions from the dataset analysis.

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