| Download | - View final version: YeastNet: deep-learning-enabled accurate segmentation of budding yeast cells in bright-field microscopy (PDF, 4.5 MiB)
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| DOI | Resolve DOI: https://doi.org/10.3390/app11062692 |
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| Author | Search for: Salem, Danny1ORCID identifier: https://orcid.org/0000-0002-2689-2997; Search for: Li, YifengORCID identifier: https://orcid.org/0000-0002-4873-6928; Search for: Xi, Pengcheng1ORCID identifier: https://orcid.org/0000-0003-3236-5234; Search for: Phenix, Hilary; Search for: Cuperlovic-culf, Miroslava1ORCID identifier: https://orcid.org/0000-0002-9483-8159; Search for: Kærn, Mads |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Abstract | Accurate and efficient segmentation of live-cell images is critical in maximizing data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of the non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast-cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labeling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub. |
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| Publication date | 2021-03-17 |
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| Publisher | MDPI |
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| Language | English |
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| Peer reviewed | Yes |
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| Identifier | app11062692 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 07dd57b6-d999-4651-8d9b-268d7bc5f8d6 |
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| Record created | 2021-06-11 |
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| Record modified | 2022-01-17 |
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