Medical Imaging

Nextmed project is a software platform for the segmentation and visualization of medical images. It consist on a series of different automatic segmentation algorithms for different anatomical structures and  a platform for the visualization of the results as 3D models.

This dataset contains the .obj and .nrrd files that correspond to the results of applying our automatic lung segmentation algorithm to the LIDC-IDRI dataset.

This dataset relates to 718 of the 1012 LIDC-IDRI scans.

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The migration of cancer cells is highly regulated by the biomechanical properties of their local microenvironment. Using 3D scaffolds of simple composition, several aspects of cancer cell mechanosensing (signal transduction, EMC remodeling, traction forces) have been separately analyzed in the context of cell migration. However, a combined study of these factors in 3D scaffolds that more closely resemble the complex microenvironment of the cancer ECM is still missing.

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Optical coherence tomography angiography (OCTA) is a novel imaging modality that allows a micron-level resolution to present the three-dimensional structure of the retinal vascular.

We propose a new multi-modality dataset, dubbed OCTA-500. It contains 500 subjects with 2 field of view (FOV) types, including OCT and OCTA volumes, 6 types of projections, 4 types of text labels and 2 types of pixel-level labels. This dataset contains more than 360K images with a size of about 80GB. 

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CHALLENGE ON ULTRASOUND BEAMFORMING WITH DEEP LEARNING (CUBDL)

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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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