Image segmentation is the process of separating a digital image into multiple sets of pixels (also called segments or image objects) and is a prerequisite step to further image analysis to locate specific objects of interest.
Currently, image segmentation is one of the main challenges of microscope image analysis, as this process is labor-intensive and prone to intra- and interobserver variability.
The good news is – new developments in machine learning algorithms have made microscopy image analysis easier than ever – finally creating a fast and unbiased route for microscope image processing in research.
In microscopy, a picture is worth a thousand words – but only when we can extract meaningful data from it. Manual analysis of microscopy images can be a long and tedious process, which is also prone to human error and bias.
Automated image analysis with machine learning algorithms involves using specialized software to extract specific data from digital microscope images. Machine learning algorithms can be trained to recognize specific objects, patterns and shapes in images to gather quantitative information, thereby optimizing and accelerating image analysis.
Using artificial intelligence (AI) to analyze microscopy images has many important advantages, including:
1. Saving you lots of time
Using machine learning algorithms allows researchers to quickly analyze vast image sets to extract meaningful information in a fraction of the time that would be required for manual image analysis.
2. Streamlining your workflow
Automated image analysis simplifies your workflow, since you would only need to provide examples of the objects to be analyzed, instead of specific parameters to define them (such as intensity threshold, size range, etc.).
3. Providing reliable and unbiased results
Manual image analysis is prone to human error, while AI-assisted analysis ensures high accuracy and unbiased results.
So, what exactly is machine learning and how does automated image analysis work?
Machine learning is a branch of artificial intelligence (AI) focused on creating algorithms with the ability to automatically learn and improve their own accuracy. Essentially, using machine learning in microscopy image analysis involves teaching a specialized software to make accurate predictions by first training it on your data. During this process, the system first learns to extract relevant features from your data. Then, it applies this information to make decisions on new data on its own.
In practical terms, this means that you can show the software how to segment your images for you, so that it will learn to correctly identify the relevant segments on its own and deliver the output you need.
Once trained, the algorithm can accurately reproduce the same output as the human user and adapt the same segmentation pattern to other images.
To perform automated image analysis using machine learning algorithms, you can simply follow these three steps:
1. Train the algorithm
Show the software how to segment your images by providing examples (such as marking background versus wanted structures).
You can ensure optimal training of your machine learning model by previewing the results and providing more examples if necessary or modify the input.
3. Load your images and get the results you need
Now, you can use your model to perform automated analysis on your images to get your desired output.
Machine learning algorithms can for example be used for the following types of analysis:
- Quantifying protein levels and distribution
- Cell profiling
- Cell division analysis
- Gene expression analysis
Digital microscopy images consist of thousands of pixels, while each pixel in the image has a specific value assigned to it. Machine learning algorithms use pixel information to calculate the size, shape and pattern of the objects appearing on the image.
Pixel classification involves assigning labels to pixels based on their features, as well as on the features of surrounding pixels in proximity and on the user’s annotations. In contrast to manually thresholding and masking an image, training a pixel classifier can help you incorporate more complex classifications and information in the analysis. Importantly, pixel classification can be used for automated image segmentation – the process of separating a digital image into regions with similar properties.
One example of image segmentation is thresholding to isolate objects or other relevant information. In automated image segmentation, the user can train a pixel classifier to assign labels so that large datasets of digital images can be automatically segmented by the software.
Training machine learning algorithms for automated segmentation involves just a few simple steps:
- Providing examples of objects of interest
- Labelling pixels and identifying your regions of interest (ROIs)
- Automatically analyzing full datasets
Once the model is trained and optimized, it provides fast and reliable results, while the algorithms can be used indefinitely and shared between colleagues and teams for further fine-tuning and training.
Manual and AI-assisted analyses have a number of important key differences, which are summarized in the table below.
|Conventional Methods||Machine Learning|
|The user needs to define the rules to quantify image objects (i.e., specifying thresholds, size range, etc.)||The user trains the software to classify pixels using examples of structures of interest.|
|Cannot be trained.||Can be trained to recognize specific objects, patterns and shapes on its own.|
|Detects structures of interest based on specific measurements.||Detects structures of interest based on intrinsic characteristics beyond measurements.|
In contrast to machine learning algorithms, manual image analysis is prone to human error – after hours of looking at microscope images of their samples, researchers may be susceptible to decision fatigue and bias. Moreover, the accuracy of threshold-based manual image analysis is highly dependent on consistent image intensity between images, as well as on sufficient image contrast, which can sometimes be difficult to achieve when capturing microscope images.
By using machine learning algorithms such as pixel classification and automated segmentation, you can eliminate the risk of human error and incorrect result interpretation. In addition, using automated image analysis to identify structures of interest makes the process exponentially faster and more efficient.
Machine learning tools such as a pixel classifier are highly versatile and can be used with a wide range of sample and image types captured with various microscopes sources. Machine learning has e.g. been successfully used to analyze live dead assays and vesicle observation experiments.
Moreover, automated image analysis can be used to process complex image objects, as models can be trained to detect objects based on intrinsic characteristics.
Machine learning algorithms can even analyze combined images from the same sample captured by different microscopes to provide further insights, as well as adapt to changing experimental and imaging conditions.
Once you have trained the model and optimized the setup, automated image analysis provides reliable and robust results. As the user, you will always be in control of the training process and you can preview your set up anytime to ensure that your supervised training is going in the right direction.
Unlike human users, machine learning algorithms are not prone to distraction and fatigue and are able to consistently produce highly reproducible and reliable results. In fact, automated image analysis using machine learning has even been shown to significantly outperform domain experts in terms of image classification accuracy, sensitivity and specificity .
AI-assisted image analysis is an easy-to-use, trainable and reliable tool that saves you time, providing instant and robust results.
What’s more, this innovative technology is also user-friendly and already available to help researchers quickly and reliably process large and complex datasets. All you need to do is provide the training data to the software to receive your desired output – in just a fraction of the time that manual analysis would require.
You can also streamline your analysis by sharing your trained algorithms with others for further training and improvement.
Machine learning is revolutionizing the way researchers collect and analyze microscopy data. Try it today and go beyond what you thought you could ever achieve with image analysis!
Then just register below and we will send you more information about using artificial intelligence in microscopy.
- Kesner, A., Laforest, R., Otazo,R., Kwak, J., Tinsu, P. Medical imaging data in the digital innovation age. Medical physics – The International Journal of Medical Physics Research and Practice (first published 05 February 2018)
- Schmidt, J., Marques, M.,R.,G., Botti, S. et al. Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials 5, Article number 83 (2019)
- Hekler, A., Utikal, J. S., Enk, A. H., Solass, W., Schmitt, M., Klode, J., Schadendorf, D., Sondermann, W., Franklin, C., Bestvater, F., Flaig, M. J., Krahl, D., von Kalle, C., Fröhling, S., & Brinker, T. J. (2019). Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. European journal of cancer (Oxford, England: 1990), 118, 91–96
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