A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy
Abstract High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-03064-y |
| _version_ | 1797275933624762368 |
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| author | Vaibhav Sharma Artur Yakimovich |
| author_facet | Vaibhav Sharma Artur Yakimovich |
| author_sort | Vaibhav Sharma |
| collection | DOAJ |
| description | Abstract High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day machine learning and deep learning algorithms, this is widely impeded by the lack of suitable datasets. To address this, here we present a purpose-created open dataset of high-content microscopy sample preparation artefact. It consists of high-content microscopy of laboratory dust titrated on fixed cell culture specimens imaged with fluorescence filters covering the complete spectral range. To ensure this dataset is suitable for supervised machine learning tasks like image classification or segmentation we propose rule-based annotation strategies on categorical and pixel levels. We demonstrate the applicability of our dataset for deep learning by training a convolutional-neural-network-based classifier. |
| first_indexed | 2024-03-07T15:21:08Z |
| format | Article |
| id | doaj.art-a138d3552bc74cdeaa1cb1f366e6be91 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-07T15:21:08Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-a138d3552bc74cdeaa1cb1f366e6be912024-03-05T17:39:20ZengNature PortfolioScientific Data2052-44632024-02-011111810.1038/s41597-024-03064-yA deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopyVaibhav Sharma0Artur Yakimovich1Center for Advanced Systems Understanding (CASUS)Center for Advanced Systems Understanding (CASUS)Abstract High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day machine learning and deep learning algorithms, this is widely impeded by the lack of suitable datasets. To address this, here we present a purpose-created open dataset of high-content microscopy sample preparation artefact. It consists of high-content microscopy of laboratory dust titrated on fixed cell culture specimens imaged with fluorescence filters covering the complete spectral range. To ensure this dataset is suitable for supervised machine learning tasks like image classification or segmentation we propose rule-based annotation strategies on categorical and pixel levels. We demonstrate the applicability of our dataset for deep learning by training a convolutional-neural-network-based classifier.https://doi.org/10.1038/s41597-024-03064-y |
| spellingShingle | Vaibhav Sharma Artur Yakimovich A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy Scientific Data |
| title | A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy |
| title_full | A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy |
| title_fullStr | A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy |
| title_full_unstemmed | A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy |
| title_short | A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy |
| title_sort | deep learning dataset for sample preparation artefacts detection in multispectral high content microscopy |
| url | https://doi.org/10.1038/s41597-024-03064-y |
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