Napari Plugin
Starting the plugin
The napari plugin has been redesigned in the newer version.
To activate the plugin, activate the napari-lattice
environment in your terminal and type napari
in the console.
The plugin is under Lattice Lightsheet Analysis
The plugin should appear on the right side. You may have to resize the window.
The functionalities with napari-lattice have been separated out into tabs:
If its configured correctly, you should see a green tick next to the tab name, else you will see a red cross.
To load an image, drag and drop it into napari. You can get some sample data here. We are using RBC_tiny.czi
as an example here.
Info
When opening a file, if you get a pop-up asking for preferred reader with czi
files, select napari-aicsimageio
Configuration
To configure any parameters, you can change the settings here:
Plugin Usage
To use the specific image for processing, you will have to select it under the Image Layer(s) to Deskew
box on the right. Here, we will click on RBC_tiny
. As its a czi file it should read the metadata
accordingly and you will see a green tick.
If you are loading a czi, the metadata fields should be populated automatically.
To Preview
the deskewed image, click Preview
and choose the appropriate channel
and time
.
You should see the deskewed image appear as an extra layer with the Preview
suffix attached to it.
Extra_info
If you look at the terminal after deskew, you should see the settings used and any other metadata associated with the dataset. It is handy for troubleshooting.
From version 1.0.3 onwards, we have an option to show the Deskewed image without actually deskewing it. It does not create a new image, but simply transforms the image in the canvas to a deskewed image. This can be useful for quick preview of the data.
To do this, once the plugin is initialized, click on Quick Deskew
.
Once you click it, you can view the deskewed image in the napari image canvas.
You may get the following warning: Non-orthogonal slicing is being requested, but is not fully supported. Data is displayed without applying an out-of-slice rotation or shear component.!
This is absolutely fine. It just means the image won't be displayed as deskewed in 2D mode. Hence, why we enable 3D mode.
Here is an example of browsing through a timeseries
The smoothness of this interactivity will depend on the storage read/write speeds and/or network speeds. For example, if the data is stored on the network, it will be slow to browse timepoints. However, if your data is on your SSD locally, the experience will be much better.
Deconvolution is primarily enabled by pycudadecon
. For this functionality, you will need the point spread function (PSF) for the corresponding channel, either simulated or experimentally derived. You can find examples here.
Important
Ensure you are using the right PSF file for each channel. The number and order of the PSF files should match the channels in the image.
After loading the image and configuring it in the Deskew
tab, select the Deconvolution
tab. When you click Enable
, you should see a green tick appear next to the name.
Under processing algorithms only cuda_gpu
and cpu
are supported. opencl_gpu
has not been implemented yet.
The next step is to select the PSF files. In this example, we will use the RBC_tiny.czi
file
- PSFs: Use the
Select files
to select multiple PSF files. As the dataset was acquired in the 48 channel, we use the 488.czi PSF file here. - Number of iterations: Try 10 if not sure and increase if needed.
- Background: Background to subtract.
- Automatic: median value of last Z slice will be used
- Second Last: median value of second last Z slice will be used. This is used in case the last Z slice is incomplete if acquisition is prematurely stopped.
- Custom: Enter a custom value
Once you are done, click Preview
at the bottom, and select timepoint or channel. You should see output from pycudadecon
printed to the terminal.
When complete, a deconvolved image will appear as an extra image layer. Below is an example of the deskewed image without (left) and with (right) deconvolution.
More instructions to be added..
More instructions to be added..
More instructions to be added..