Deep learning tool to revolutionize microscopy

An AI tool developed at the University of Gothenburg offers new opportunities for analyzing images taken with microscopes. A study shows that the tool can fundamentally change microscopy and pave the way for new discoveries and areas of use within both research and industry.

Photo
The image shows how a neural network is used to retrieve interesting information from a microscope image.
Source: Aykut Argun

The focus of the study is deep learning, a type of artificial intelligence (AI) and machine learning that we all interact with daily, often without thinking about it. For example when a new song on Spotify pops up that is similar to songs we have previously listened to or when our mobile phone camera automatically finds the best settings and corrects colors in a photo.

"Deep learning has taken the world by storm and has had a huge impact on many industries, sectors and scientific fields. We have now developed a tool that makes it possible to utilize the incredible potential of deep learning, with focus on images taken with microscopes," says Benjamin Midtvedt, a doctoral student in physics and the main author of the study.

Deep learning can be described as a mathematical model used to solve problems that are difficult to tackle using traditional algorithmic methods. In microscopy, the great challenge is to retrieve as much information as possible from the data-packed images, and this is where deep learning has proven to be very effective.

The tool that Midtvedt and his research colleagues have developed involves neural networks learning to retrieve exactly the information that a researcher wants from an image by looking through a huge number of images, known as training data. The tool simplifies the process of producing training data compared with having to do so manually, so that tens of thousands of images can be generated in an hour instead of a hundred in a month.

"This makes it possible to quickly extract more details from microscope images without needing to create a complicated analysis with traditional methods. In addition, the results are reproducible, and customized, specific information can be retrieved for a specific purpose."

For example, the tool allows the user to decide the size and material characteristics for very small particles and to easily count and classify cells. The researchers have already demonstrated that the tool can be used by industries that need to purify their emissions since they can see in real time whether all unwanted particles have been filtered out.

The researchers are hopeful that in the future the tool can be used to follow infections in a cell and map cellular defense mechanisms, which would open up huge possibilities for new medicines and treatments. "We have already seen major international interest in the tool. Regardless of the microscopic challenges, researchers can now more easily conduct analyses, make new discoveries, implement ideas and break new ground within their fields."

Subscribe to our newsletter

Related articles

Algorithm designs soft robots that sense

Algorithm designs soft robots that sense

Deep learning technique optimizes the arrangement of sensors on a robot’s body to ensure efficient operation.

Using AI to generate 3D holograms in real-time

Using AI to generate 3D holograms in real-time

A new method called tensor holography could enable the creation of holograms for virtual reality, 3D printing, medical imaging, and more — and it can run on a smartphone.

AI method can detect precursors to cervical cancer

AI method can detect precursors to cervical cancer

Using AI and mobile digital microscopy, researchers hope to create screening tools that can detect precursors to cervical cancer in women in resource-limited settings.

AI, holographic microscopy beat scientists at analyzing immunotherapy​

AI, holographic microscopy beat scientists at analyzing immunotherapy​

AI is helping researchers decipher images from a new holographic microscopy technique needed to investigate a key process in cancer immunotherapy “live” as it takes place.

AI identifies 'ugly ducklings' to catch skin cancer

AI identifies 'ugly ducklings' to catch skin cancer

Deep learning-based system enables dermatologist-level identification of suspicious skin lesions from smartphone photos, allowing better screening.

Designing medical deep learning systems

Designing medical deep learning systems

Researchers have analysed whether better design of deep learning studies can lead to the faster transformation of medical practices.

How to train a robot - using AI and supercomputers

How to train a robot - using AI and supercomputers

Computer scientists use TACC systems to generate synthetic objects for robot training.

Biomedical research: deep learning outperforms machine learning

Biomedical research: deep learning outperforms machine learning

Deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

Neural network learns when it should not be trusted

Neural network learns when it should not be trusted

Researchers have developed a way for deep learning neural networks to rapidly estimate confidence levels in their output.

Popular articles

Subscribe to Newsletter