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3 projects in result set.
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 |
Fiber Tractography for Finite-Element Modeling of Transversely Isotropic Tissues
- This project demonstrates the process for fiber tractography of complex biological tissues with transverse isotropy, such as tendon and muscle. This is important for finite element studies of these tissues, as the fiber direction must be specified in the constitutive model. This project contains code, models, and data that can be used to reproduce the results of our publication on this technique. The supplied instructional videos will enable researchers to easily and efficiently apply this method to a variety of other tissues. The software used in the fiber tractography process and demonstrated in this project is Matlab, Autodesk Inventor (free for educators), and Autodesk Simulation CFD (free for educators). Full demonstrations and process instructions can be found in the 7 videos posted at https://vimeo.com/album/3414604:
Contents:
Chapter 1: Introduction (2:35)
This video introduces the CFD fiber tractography software pipeline
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Chapter 2: Supplementary materials code, models and data (20:21)
This video shows the shared models, code, and data posted online at simtk.org/m3lab_cfd4fea.
Chapter 3: Finite element simulations (5:38)
This video shows finite element simulations using the fiber mapping process.
Chapter 4: Iliacus example walkthrough (21:38)
This video shows the step-by-step process for fiber mapping the iliacus muscle (a hip flexor).
Chapter 5: Bflh example walkthrough (12:09)
This video shows the step-by-step process for fiber mapping the biceps femoris longhead muscle (a hamstring).
Chapter 6: Autodesk Inventor segmentation (9:09)
This video shows how to do segmentation of medical images in Autodesk Inventor in order to simplify the solid model for the CFD and FEA software.
Chapter 7: Curved inlet surfaces (6:28)
This video shows how to create curved inlet surfaces for use in Autodesk Simulation CFD. | |
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Activity Percentile: 35.23 Registered: 2015-05-28 18:52 |
WraptMor
- WraptMor is a model that can be used to estimate ligament fiber insertion-to-insertion length with wrapping around osseous surfaces. The code for the WraptMor model and examples that demonstrate the model's usage can be found in SourceCode. See the Wiki page for information on the required packages needed to run the WraptMor code, and an example demonstrating the code's usage.
Citing - Please cite this article if you use the WraptMor code or approach:
Zaylor, William, and Halloran, Jason P. (May 6, 2021). "WraptMor: Confirmation of an Approach to Estimate Ligament Fiber Length and Reactions With Knee-Specific Morphology." ASME. J Biomech Eng. August 2021; 143(8): 081012. https://doi.org/10.1115/1.4050810 | |
Registered: 2020-07-31 17:04 |