Medical Imaging

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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This dataset contains 91 computed tomography pulmonary angiograms positive for pulmonary embolism. At least one experience radiologist has segmented all clots in each of the scans. The dataset was originated for the ISBI challenge cad-pe.

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The purpose of this challenge is to provide standardization of methods for assessing and benchmarking deep learning approaches to ultrasound image formation from ultrasound channel data that will live beyond the challenge.

Last Updated On: 
Mon, 11/25/2024 - 00:59
Citation Author(s): 
Muyinatu A. Lediju Bell, Jiaqi Huang, Alycen Wiacek, Ping Gong, Shigao Chen, Alessandro Ramalli, Piero Tortoli, Ben Luijten, Massimo Mischi, Ole Marius Hoel Rindal, Vincent Perrot, Hervé Liebgott, Xi Zhang, Jianwen Luo, Eniola Oluyemi, Emily Ambinder

基于波动算法的快速视网膜分割

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Hyperspectral (HS) imaging presents itself as a non-contact, non-ionizing and non-invasive technique, proven to be suitable for medical diagnosis. However, the volume of information contained in these images makes difficult providing the surgeon with information about the boundaries in real-time. To that end, High-Performance-Computing (HPC) platforms become necessary. This paper presents a comparison between the performances provided by five different HPC platforms while processing a spatial-spectral approach to classify HS images, assessing their main benefits and drawbacks.

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