Assessing microscope image focus quality with deep learning Article Swipe
Samuel Yang
,
Marc Berndl
,
D. Michael Ando
,
Mariya Barch
,
Arunachalam Narayanaswamy
,
Eric Christiansen
,
Stephan Hoyer
,
C. Roat
,
Jane Hung
,
Curtis Rueden
,
Asim Shankar
,
Steven Finkbeiner
,
Philip Nelson
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1186/s12859-018-2087-4
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1186/s12859-018-2087-4
Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12859-018-2087-4
- https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-018-2087-4
- OA Status
- gold
- Cited By
- 151
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2795106634
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2795106634Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s12859-018-2087-4Digital Object Identifier
- Title
-
Assessing microscope image focus quality with deep learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-03-02Full publication date if available
- Authors
-
Samuel Yang, Marc Berndl, D. Michael Ando, Mariya Barch, Arunachalam Narayanaswamy, Eric Christiansen, Stephan Hoyer, C. Roat, Jane Hung, Curtis Rueden, Asim Shankar, Steven Finkbeiner, Philip NelsonList of authors in order
- Landing page
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https://doi.org/10.1186/s12859-018-2087-4Publisher landing page
- PDF URL
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https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-018-2087-4Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-018-2087-4Direct OA link when available
- Concepts
-
Artificial intelligence, Focus (optics), Computer science, Image quality, Computer vision, Autofocus, Pixel, Pattern recognition (psychology), Deep learning, Artificial neural network, Binary image, Task (project management), Image processing, Image (mathematics), Optics, Physics, Management, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
151Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 13, 2023: 20, 2022: 19, 2021: 33Per-year citation counts (last 5 years)
- References (count)
-
24Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.training | 40, 56 |
| abstract_inverted_index.annotated | 39 |
| abstract_inverted_index.available | 61, 72 |
| abstract_inverted_index.certainty | 25 |
| abstract_inverted_index.defocused | 31 |
| abstract_inverted_index.different | 47 |
| abstract_inverted_index.framework | 53 |
| abstract_inverted_index.immediate | 74 |
| abstract_inverted_index.precision | 16 |
| abstract_inverted_index.precludes | 33 |
| abstract_inverted_index.approaches | 19 |
| abstract_inverted_index.microscope | 8 |
| abstract_inverted_index.prediction | 59 |
| abstract_inverted_index.generalizes | 45 |
| abstract_inverted_index.patch-level | 22 |
| abstract_inverted_index.pre-trained | 69 |
| abstract_inverted_index.out-of-focus | 7 |
| abstract_inverted_index.predictions. | 26 |
| abstract_inverted_index.CellProfiler. | 80 |
| abstract_inverted_index.interpretable | 21 |
| abstract_inverted_index.synthetically | 30 |
| abstract_inverted_index.classification | 5 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5090108597 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I1291425158 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.99313658 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |