Medical Imaging
CHALLENGE ON ULTRASOUND BEAMFORMING WITH DEEP LEARNING (CUBDL)
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n/a
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Some examples of the non-public data set ImageCLEF 2019 VQA-Med, including training, validation and test part.
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This dataset was used to investigate numerical methods of integration of the Frenet-Serret equations as applied to the study of vessel shape. This data is a compliation of previously published data from the following papers:
A. V. Kamenskiy, J. N. MacTaggart, I. I. Pipinos, et al., “Three-dimensional geometry of the human carotid artery,” Journal of Biomechanical Engineering, vol. 134, no. 6, p. 064592, 2012.
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Features Extracted from BraTS 2012-2013
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An accurate analysis of fluid–structure interaction (FSI) at compliant arteries via ultrasound (US) imaging and numerical modeling is a limitation of several studies. In this study, we propose a deep learning-based boundary detection and compensation (DL-BDC) technique that can segment vessel boundaries by harnessing the convolutional neural network and wall motion compensation in near-wall flow dynamics. The segmentation performance of the technique is evaluated through numerical simulations with synthetic US images and in vitro experiments.
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The presented database contains thermal images (thermograms) of the plantar region. The database was obtained from 122 subjects with a diabetes diagnosis (DM group) and 45 non-diabetic subjects (control group). The relevance of this database consists in to study how the temperature is distributed in the plantar region of both groups and how their differences can be measured. Previous reports in the literature have established that an increase in the plantar temperature is associated with a higher ulceration risk.
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