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Focus on Bio-Image Informatics

Overview of attention for book
Attention for Chapter 9: Challenges and Benchmarks in Bioimage Analysis.
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Chapter title
Challenges and Benchmarks in Bioimage Analysis.
Chapter number 9
Book title
Focus on Bio-Image Informatics
Published in
Advances in anatomy embryology and cell biology, May 2016
DOI 10.1007/978-3-319-28549-8_9
Pubmed ID
Book ISBNs
978-3-31-928547-4, 978-3-31-928549-8
Authors

Michal Kozubek

Editors

Winnok H. De Vos, Sebastian Munck, Jean-Pierre Timmermans

Abstract

Similar to the medical imaging community, the bioimaging community has recently realized the need to benchmark various image analysis methods to compare their performance and assess their suitability for specific applications. Challenges sponsored by prestigious conferences have proven to be an effective means of encouraging benchmarking and new algorithm development for a particular type of image data. Bioimage analysis challenges have recently complemented medical image analysis challenges, especially in the case of the International Symposium on Biomedical Imaging (ISBI). This review summarizes recent progress in this respect and describes the general process of designing a bioimage analysis benchmark or challenge, including the proper selection of datasets and evaluation metrics. It also presents examples of specific target applications and biological research tasks that have benefited from these challenges with respect to the performance of automatic image analysis methods that are crucial for the given task. Finally, available benchmarks and challenges in terms of common features, possible classification and implications drawn from the results are analysed.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 38%
Researcher 4 31%
Student > Bachelor 1 8%
Student > Master 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 1 8%
Readers by discipline Count As %
Computer Science 5 38%
Agricultural and Biological Sciences 2 15%
Engineering 2 15%
Materials Science 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Other 0 0%
Unknown 2 15%