Automatic Cell Counting in Flourescent Microscopy Using Deep Learning Article Swipe
R. Morelli
,
L. Clissa
,
Marco Dalla
,
Marco Luppi
,
L. Rinaldi
,
A. Zoccoli
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.01141
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.01141
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subject to arbitrariness due to the operator's interpretation of the borderline cases. We propose a Machine Learning approach that exploits a fully-convolutional network in a binary segmentation fashion to localize the objects of interest. Counts are then retrieved as the number of detected items. Specifically, we adopt a UNet-like architecture leveraging residual units and an extended bottleneck for enlarging the field-of-view. In addition, we make use of weighted maps that penalize the errors on cells boundaries increasingly with overcrowding. These changes provide more context and force the model to focus on relevant features during pixel-wise classification. As a result, the model performance is enhanced, especially in presence of clumping cells, artifacts and confounding biological structures. Posterior assessment of the results with domain experts confirms that the model detects cells of interest correctly. The model demonstrates a human-level ability inasmuch even erroneous predictions seem to fall within the limits of operator interpretation. This qualitative assessment is also corroborated by quantitative metrics as an ${F_1}$ score of 0.87. Despite some difficulties in interpretation, results are also satisfactory with respect to the counting task, as testified by mean and median absolute error of, respectively, 0.8 and 1.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.01141
- https://arxiv.org/pdf/2103.01141
- OA Status
- green
- Cited By
- 7
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3134286047
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3134286047Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2103.01141Digital Object Identifier
- Title
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Automatic Cell Counting in Flourescent Microscopy Using Deep LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-02-24Full publication date if available
- Authors
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R. Morelli, L. Clissa, Marco Dalla, Marco Luppi, L. Rinaldi, A. ZoccoliList of authors in order
- Landing page
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https://arxiv.org/abs/2103.01141Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2103.01141Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2103.01141Direct OA link when available
- Concepts
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Microscopy, Materials science, Artificial intelligence, Computer science, Nanotechnology, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2024: 1, 2023: 4, 2021: 2Per-year citation counts (last 5 years)
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11Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.predictions | 185 |
| abstract_inverted_index.qualitative | 196 |
| abstract_inverted_index.researchers | 11 |
| abstract_inverted_index.structures. | 158 |
| abstract_inverted_index.architecture | 93 |
| abstract_inverted_index.corroborated | 200 |
| abstract_inverted_index.demonstrates | 178 |
| abstract_inverted_index.difficulties | 212 |
| abstract_inverted_index.experimental | 21 |
| abstract_inverted_index.increasingly | 120 |
| abstract_inverted_index.quantitative | 202 |
| abstract_inverted_index.satisfactory | 218 |
| abstract_inverted_index.segmentation | 70 |
| abstract_inverted_index.Specifically, | 88 |
| abstract_inverted_index.arbitrariness | 46 |
| abstract_inverted_index.overcrowding. | 122 |
| abstract_inverted_index.respectively, | 234 |
| abstract_inverted_index.field-of-view. | 104 |
| abstract_inverted_index.interpretation | 51 |
| abstract_inverted_index.time-consuming | 8 |
| abstract_inverted_index.classification. | 139 |
| abstract_inverted_index.interpretation, | 214 |
| abstract_inverted_index.interpretation. | 194 |
| abstract_inverted_index.fully-convolutional | 65 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |