Deep learning to analyse microscopy images
Author(s) -
Guillaume Jacquemet
Publication year - 2021
Publication title -
the biochemist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.126
H-Index - 7
eISSN - 1740-1194
pISSN - 0954-982X
DOI - 10.1042/bio_2021_167
Subject(s) - artificial intelligence , computer science , focus (optics) , segmentation , feature (linguistics) , image segmentation , computer vision , noise (video) , process (computing) , noise reduction , image (mathematics) , pattern recognition (psychology) , microscopy , linguistics , philosophy , physics , optics , operating system
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.
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