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All Topics > Biological Applications > Cell |
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4 projects in result set.
Practical Annotation and Exchange of Virtual Anatomy
- Representation of anatomy in a virtual form is at the heart of clinical decision making, biomedical research, and medical training. Virtual anatomy is not limited to description of geometry but also requires appropriate and efficient labeling of regions - to define spatial relationships and interactions between anatomical objects; effective strategies for pointwise operations - to define local properties, biological or otherwise; and support for diverse data formats and standards - to facilitate exchange between clinicians, scientists, engineers, and the general public. Development of aeva, a free and open source software package (library, user interfaces, extensions) capable of automated and interactive operations for virtual anatomy annotation and exchange, is in response to these currently unmet requirements. This site serves for aeva outreach, including dissemination the software and use cases. The use cases drive design and testing of aeva features and demonstrate various workflows that rely on virtual anatomy.
aeva downloads:
Downloads (https://simtk.org/frs/?group_id=1767)
Kitware data repository (https://data.kitware.com/#folder/5e7a4690af2e2eed356a17f2)
aeva documentation:
Guides and tutorials (https://aeva.readthedocs.io)
aeva videos:
Short instructions (https://www.youtube.com/channel/UCubfUe40LXvBs86UyKci0Fw)
aeva source code:
Kitware source code repository (https://gitlab.kitware.com/aeva)
aeva forum:
Forums (https://simtk.org/plugins/phpBB/indexPhpbb.php?group_id=1767 ) | |
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Registered: 2019-08-28 01:27 |
DeepCell: Deep convolutional neural networks for image segmentation
- The assignment of a cellular identity to individual pixels in microscopy images is a key technical challenge for many live-cell experiments. Traditional approaches to this image segmentation problem have relied on standard computer vision techniques, such as thresholding, morphological operations, and the watershed transform. While these approaches have enabled the analysis of numerous experiments, they are limited in their robustness and in applicability. Here, we show that deep convolutional neural networks, a supervised machine learning method, can robustly segment the cytoplasms of individual bacterial and mammalian cells. This approach automates the analysis of thousands of bacterial cells and leads to more accurate quantification of localization based fluorescent reporters in mammalian cells. In addition, this approach can also simultaneously segment and identify different mammalian cell types in co-cultures. Deep convolutional neural networks have had a transformative impact on the problem of image classification, and we anticipate that they will have a similar impact for live-cell imaging experiments.
Visit our webpage at http://covertlab.github.io/DeepCell | |
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Activity Percentile: 80.30 Registered: 2015-11-16 19:58 |
BlurLab -- 3D Microscopy Simulation Package
- BlurLab is an easy to use platform for generating simulated fluorescence microscopy data for use in mechanistic modeling visualization, image comparison, and hypothesis testing. The software accepts the 3D positions, intensities and labels of fluorescing objects that are produced by an underlying mechanistic model and transforms them into high quality simulated images. The program includes full 3D convolution with realistic (or even measured) point spread functions; inclusion of thermal, shot and custom noise spectra; simulations of mean and fully stochastic photobleacing; the ability to view scenes in wide-field and TIRF, and perform Z-slicing; and the ability to simulate FRAP experiments.
The software provides a platform for adjusting and saving these simulated images, as well as a number of helpful, semi-automated features to make image simulation easy and less error prone. | |
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Activity Percentile: 77.27 Registered: 2011-08-05 01:17 |
Membrane Segmentation Toolkit
- The Membrane Segmentation Toolkit implements the approach to identifying plasma membrane voxels in light microscopy images of cells described in Kasson et al., Bioinformatics. 2005 Oct 1;21(19):3778-86. | |
Activity Percentile: 0.00 Registered: 2007-04-22 01:23 |