From pixels to insights: Machine learning and deep learning for bioimage analysis

Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate...

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Bibliographic Details
Published in:	BioEssays Vol. 46; no. 2; pp. e2300114 - n/a
Main Authors:	Jan, Mahta, Spangaro, Allie, Lenartowicz, Michelle, Mattiazzi Usaj, Mojca
Format:	Journal Article
Language:	English
Published:	United States Wiley Subscription Services, Inc 01-02-2024
Subjects:	bioimage analysis bioimage analysis tools Biological analysis Cellular structure Deep Learning Feature extraction image processing Image Processing, Computer-Assisted - methods Learning algorithms Machine Learning microscopy Microscopy, Fluorescence - methods Software Workflow image processing deep learning bioimage analysis tools microscopy bioimage analysis machine learning
Online Access:	Get full text
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Summary:	Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user‐friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field. Machine learning and deep learning have revolutionized bioimage analysis, automating and enhancing tasks like image preprocessing, object segmentation and tracking, feature extraction, and classification. This review showcases the pivotal role these approaches have played in the field, and highlights user‐friendly bioimage analysis tools for biologists without extensive computational expertise.
Bibliography:	Mahta Jan and Allie Spangaro contributed equally to this work. ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1
ISSN:	0265-9247 1521-1878
DOI:	10.1002/bies.202300114