YeastNet: deep-learning-enabled accurate segmentation of budding yeast cells in bright-field microscopy

NRC publication: Yes

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 6; p. 2692
Main Authors Salem, Danny, Li, Yifeng, Xi, Pengcheng, Phenix, Hilary, Cuperlovic-culf, Miroslava, Kærn, Mads
Format Journal Article
LanguageEnglish
Published Basel MDPI 17.03.2021
MDPI AG
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ISSN2076-3417
2076-3417
DOI10.3390/app11062692

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Summary:NRC publication: Yes
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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ISSN:2076-3417
2076-3417
DOI:10.3390/app11062692