B1 Non-refereed article in a scientific journal
Deep learning to analyse microscopy images
Authors: Jacquemet Guillaume
Publisher: Portland Press Ltd
Publication year: 2021
Journal: Biochemist
Journal name in source: Biochemist
Volume: 43
Issue: 5
First page : 60
Last page: 64
DOI: https://doi.org/10.1042/bio_2021_167
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/69310675
Artificial intelligence (AI)-powered algorithms are now influencing many aspects of our day-to-day life, from providing movies/music recommendations to controlling self-driving cars. These algorithms are also increasingly used in the lab to aid biomedical research. In particular, the ability to analyse and process images using AI is slowly revolutionizing the quality and quantity of data we collect from microscopy images. In fact, AI-based algorithms can now be applied to perform virtually any high-performance image analysis tasks such as classifying images, detecting and segmenting objects, aligning images or improving image quality by removing noise or increasing image resolution. This short feature article briefly underlies the principles behind using AI algorithms to analyse microscopy images with a specific focus on segmentation and denoising.
Downloadable publication This is an electronic reprint of the original article. |