24 Aug 2020
![2DCellTrackingExample]( "2DCellTrackingExample")
2D Cell Tracking Example
A 2D cell tracking example from a multichannel time series data set. Contains a segmentation recipe, modules needed to track the cells through time, and a video of the finished result.
The images are matched
2D phase contrast microscopy and EFFP labeled fluorescent microscopy
across 61 time points. The fluorescent signal comes from a cell cycle
regulator called cyclin-dependent protein kinase regulator (Clb2). Raw data are found here:
http://cellimagelibrary.org/images/35769. Tips:
- You must use Amira for Cell Biology to load and replicate this project.
- Toggle Auto-Refresh if you want to use the Process Time Series module.
- The Time module governs all of the time sliders throughout the project and this port is pinned.
- The pixel size is 0.2134 um x 0.2134 um.
- The project was created with Spatial Units disabled. This is recommended if there are any errors when loading the data with Spatial Units enabled.
- The tracks display slightly offset from the images. If you can’t see the tracks, try moving the camera to the other side of the 2D image.
- You can recreate the video with the animation director.
- The Recipe Player may need to be provided with a corrected recipe path in order to work on your system. Be sure that the recipe path is "{SCRIPTDIR}/BuddingYeastSegmentation_2June2020-files/BuddingYeastSegmentation_2June2020.hxrecipe" and replace "
{SCRIPTDIR}" with the path to where you loaded the project from.
Recipe steps:
- Load fluorescent and phase contrast channels.
- Run Non-local means filter on phase contrast channel.
- Run gaussian filter on output of step 2.
- Extract and subtract background image from output of step 3.
- Use Variance module on the output of step 4 to find slow-changing bright background to make segmentation easier.
- Use arithmetic to perform thresholding on the output of step 5.
- Morphological operators on the output of step 6 to remove small particles and holes. Also inverts this result. This will be a reverse mask for later.
- Change input to fluorescent channel and run non-local means filter.
- Adaptive Thresholding on output of step 8 to select high intensity regions.
- Morphological operators on the output of step 9 to remove small spots, remove small holes, and separate cells using Separate Objects module.
- Labeling and addition of the output of step 7 using an arithmetic module.
- Marker based Watershed Inside Mask module using output of step 11 as markers, output of step 10 as a mask, and the image data to provide a gradient image.
- Finally, the background (from step 7) is removed using arithmetic and the output is a label field of separated and labeled cells.
David Ball, Jean Peccoud (2011) CIL:35769, Saccharomyces cerevisiae. CIL. Dataset. https://doi.org/doi:10.7295/W9CIL35769