ijs-Perkin Elmer Operetta CLS, Stitching And Export

Updated 1,023 Days AgoPublic

Actions

We obtained a Perkin Elmer Opereta CLS mid 2017 to address medium-high throughput live imaging needs.

The system is fully integrated with its software and some work was needed to extract the images for use in ImageJ

Parameters

Select your directory with your exported images

This is the directory where the Harmony Software extracted the tif files and .xml file, for example D:/MyData/images/

Select directory to save images

Directory to save the new images, if set to default, it will resave into the same fodler as above, in a folder called output

Resize Factor

An integer larger than 1, that defines the downsampling factor, in case you just want to check the data, it can make processing a lot faster

Export file(s) as

Defines whether to fuse the fields of a well together or to export each field as a separate image

Only Process Selected Wells

Opens a GUI to select which well(s) to export

X Y W H of box to extract

Defines a string representing a rectangle with
X= top left corner x coordinate, in pixels
Y = top left corner y coordinate, in pixels
W = rectangle width in pixels
H = rectangle height in pixels

Project Z Slices

If the datasets have slices, this allows for the exporter to project them

Z Projection Method

You can choose the projection method to use, as in Image → Stacks → Z Project...
Choices are "Max Intensity", "Sum Slices", "Average Intensity", "Standard Deviation", "Median"

Export Sub Fields

If unchecked, will export all fields, otherwise it will export the selected fields, see below

Fields(s) to Export

The choice of fields to export, you can select multiple fields by specifying either comma-separated numbers or ranges using a dash

Export Sub Channels

If unchecked, will export all channels, otherwise see below

Channel(s) to Export

Comma separated list of channels to export

Export Sub Slices

If unchecked, exports all slices, otherwise see below

Slice(s) to Export

Comma or dash separated list of slices to export (that will be z projected if checked above) (use : to specify range)

Export Sub Timepoint

If unchecked, exports all timepoints, otherwise see below

Timepoint(s) to Export

Comma or dash separated list of timepoints to export

Installation

The easiest way is to use BIOP update site

Alternatively, you can download the .groovy script from our GIT repository.
rOPERETTAIMPORT
and add it to your plugins > Scripts folder or simply open it and run it directly in Fiji

Dependencies

We make use of GPARS: A Concurrency & Parallelism Framework to take advantage of the maximum possible performance of our workstations.
But for that we need to make sure that the following libraries are located in the jars folder of Fiji/ImageJ

• groovy-xml.jar - Contains the XML parsing library we use to read the Operetta Metadata
• groovy-swing.jar - Contains what is needed to create a GUI to select the wells to process
• gpars.jar - Is the concurrency framework that allows us to have fun with parallel loops
• jsr166y.jar - Is the java library that GPARS depends on

Use

Upon saving a series of images using the Export button

We obtain a folder like so

This inside the Images folder, we have all the wells, fields, channels, slices and timepoints as individual compressed tiffs

More importantly we have a Index.idx.xml that contains all the information on the acquisition

When you run the script, simply provide the location of the Images folder and the downsample factor you want (if any)

Rationale Behind the Script

Missing Images

One major issue is that images that for whatever reason could not be acquired would not appear, so simple imports into ImageJ was not possible.
We needed a way to parse the data in order to know the real dimensions of the dataset that was acquired. This way we can create a clean hyperstack where missing images are at their correct locations

Compressed Tiffs

The exported output contains tiffs that are lzip-compressed. For ImageJ, this means that there is a strong overhead to unzip the file before having access to its pixel data (150ms vs 10ms for opening an uncompressed tiff of the same dimensions). This prompted us to parallelize the opening of each series to maximize hard drive and processor bandwidth.

Stitching

Users wanted to be able to view the stitched versions of their multiple fields in ImageJ, so this script assembles the images based on the stage coordinates given by the Operetta. As of yet, there is no smart stitching or computation done.

Known Issues

Too many threads are being called

We are using a multi threaded approach to process as many wells in parallel. within each of these threads, we call upon more threads to handle each timepoint separately. This way, we can both make use of all the RAM (Wells in parallel) and the CPU to read the tiffs (Timepoints in parallel)
However, this creates more threads than it should. An issue was filed under https://github.com/GPars/GPars/issues/55
This is no problem per se, but your PC will be very slow until it finishes... Get in touch with the BIOP if you encounter issues.

Best

Oli