How to ensure fast, accurate and repeatable cell counting

24.9.2019
How-tos
How automated cell counting can speed up your research

Cell counting is a common task in many life science applications and for over 100 years the process has been primarily manual. These manual processes can be time-consuming and unrepeatable due to lack of statistical robustness and subjectivity of cell definition among researchers. Automating the cell counting process is the key factor to obtain reproducible results across various samples and users.  

Manual counting of cells in the above image by 10 different individuals resulted in numbers ranging from 15 to 20 proving the human subjectivity of cell definition.
Manual counting of cells in the above image by 10 different individuals resulted in numbers ranging from 15 to 20 proving the human subjectivity of cell definition.

Today’s challenges: dealing with different software packages  

While today’s microscopes are designed for automated imaging, the process of extracting information from these micrographs can be challenging. There are excellent commercial and open source software packages that offer useful tools for image processing, but most researchers barely scratch their surface. This is partly due to the bulky nature of these software packages that come with steep learning curve. In addition, most life scientists are skilled at understanding the biological challenges but not trained at coding image processing algorithms, especially with machine learning and artificial intelligence. A good portion of code is available in the open source domain, but a typical researcher is not comfortable working with code. Therefore, valuable research time gets wasted in performing manual tasks or dealing with multiple fragmented software packages.

What APEER offers to solve these challenges

APEER addresses these issues by providing an easy to use online platform APEER for the researcher to build custom applications (Workflows) using individual pieces of software (Modules). For example, Supervised Segmentation Trainer and Segmentation Mask Generator (Predictor) modules on APEER can be used to build cell counter workflows. These modules are based on U-Net, a convolutional neural network that was developed for biomedical image segmentation. They are designed to scale up computational resources by leveraging GPU acceleration in the cloud.

Supervised Segmentation Trainer and Segmentation Mask Generator modules on APEER
Supervised Segmentation Trainer and Segmentation Mask Generator modules on APEER

How a cell counting application is structured

Although U-Net has proven to be ideal for many applications, its accuracy depends on the amount of training data, as it is the case with any deep learning approach. Therefore, a traditional image processing approach is preferred for single images or smaller datasets e.g. single 3D stack or a 96 well plate. Traditional image processing involves a handful of image processing operations that progressively extract information from images. For example, cell counting using multichannel images often involves extraction of the blue (DAPI) channel as a first step. This image then gets thresholded to detect cells, refined to fill missing pixels & separate overlapping cells (e.g. Watershed) and analyzed to report numbers of cells and their individual measurements, respectively.

Step 1: Collect / Load image and extract DAPI channel image
Collect / Load image and extract DAPI channel image on your microscopy image

Step 2: Extract relevant channels (DAPI)
Extract relevant channels (DAPI) on your microscopy image

Step 3: Threshold to detect pixels with cells
Threshold to detect pixels with cells on your microscopy image

Step 4: Refine by filling missing pixels (holes) and separating cells using Watershed
Refine by filling missing pixels (holes) and separating cells on your microscopy image using Watershed

Step 5: Measure cells
Results of the measured cells
Results of the measured cells

Step 6: Plot results
plot results
Benefits of an automated cell counting workflow

Each of the above tasks can be performed individually in most image processing software packages but the researcher needs to carefully document settings for each step during execution in order to repeat the experiment on multiple images. A minor error is setting up parameters for a single step may yield statistically varying results due to propagation of errors through each step in the process. These human errors can be easily overcome by automating the process. An end to end automated workflow to perform these tasks can be easily put together on APEER using just two versatile modules, Advanced Threshold and Particle Analyzer 2D. The results from this workflow can be seen here.

Workflow with two modules: Advanced Threshold and Particle Analyzer 2D
Workflow with two modules: Advanced Threshold and Particle Analyzer 2D

Alternatively, the researcher can use one of the preconfigured public workflows like the Cell Counter and Analyzer developed by the APEER team. This approach provides the researcher more control over individual modules that make up the workflow. The researcher can further customize the workflow by swapping, deleting or adding modules. The results from this workflow can be seen here.

Cell Counter and Analyzer Workflow on APEER
Cell Counter and Analyzer Workflow on APEER
Cell Counter and Analyzer Workflow on APEER

In the next step the results can be published or shared with selected individuals who can repeat the same experiment using the same settings without the worry of defining every parameter in the workflow.

Share your workflow results on APEER
Share your workflow results on APEER

In summary, with APEER, applications can be customized by researchers for their specific jobs to be done. These customized applications can then be automated where the results can be shared for download or online visualization by their peers. APEER not only saves valuable research time but also provides a platform for highly repeatable results by researchers and their peers.

 

Sreenivas Bhattiprolu

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