Title | A deep learning workflow for quantification of Micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation |
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Type | Journal article |
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Authors | Anand Panchbhai, Munuse Ceyda Ishanzadeh, Smarana Pankanti, Ahmed Sidali, Nadeeen Solaiman, Radhakrishnan Kanagaraj, John J. Murphy and Kalpana Surendranath |
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Abstract | The cytokinesis block micronucleus assay is widely used for measuring/scoring/counting micronuclei, a marker of genome instability in cultured and primary cells. Though a gold standard method, this is a laborious and time-consuming process with person-to-person variation observed in quantification of micronuclei. We report in this study the utilisation of a new deep learning workflow for detection of micronuclei in DAPI stained nuclear images. The proposed deep learning framework achieved an average precision of >90% in detection of micronuclei. This proof of principle investigation in a DNA damage studies laboratory supports the idea of deploying AI powered tools in a cost-effective manner for repetitive and laborious tasks with relevant computational expertise. These systems will also help improving the quality of data and wellbeing of researchers. |
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Keywords | MN |
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| genome instability |
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| DAPI |
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| cancer diagnostics |
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| MN detection |
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| Artificial Intelligence |
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Article number | 107447 |
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Journal | Computer Methods and Programs in Biomedicine |
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Journal citation | 232 |
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ISSN | 0169-2607 |
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| 1872-7565 |
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Year | 2023 |
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Publisher | Elsevier |
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Accepted author manuscript | License CC BY-NC-ND 4.0 File Access Level Open (open metadata and files) |
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Publisher's version | File Access Level Open (open metadata and files) |
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Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2023.107447 |
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PubMed ID | 36889248 |
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Web address (URL) | https://doi.org/10.1016/j.cmpb.2023.107447 |
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Publication dates |
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Published online | 26 Feb 2023 |
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Published in print | Apr 2023 |
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