Prangemeier, Tim ; Wildner, Christian ; Françani, André O. ; Reich, Christoph ; Koeppl, Heinz (2022):
Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning. (Postprint)
In: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB),
Darmstadt, IEEE, IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB), Online, 27.-29.10.2020, ISBN 978-1-7281-9468-4,
DOI: 10.26083/tuprints-00021524,
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Item Type: | Conference or Workshop Item |
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Origin: | Secondary publication service |
Status: | Postprint |
Title: | Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning |
Language: | English |
Abstract: | Cell segmentation is a major bottleneck in extracting quantitative single-cell information from microscopy data. The challenge is exasperated in the setting of microstructured environments. While deep learning approaches have proven useful for general cell segmentation tasks, existing segmentation tools for the yeast-microstructure setting rely on traditional machine learning approaches. Here we present convolutional neural networks trained for multiclass segmenting of individual yeast cells and discerning these from cell-similar microstructures. We give an overview of the datasets recorded for training, validating and testing the networks, as well as a typical use-case. We showcase the method's contribution to segmenting yeast in microstructured environments with a typical synthetic biology application in mind. The models achieve robust segmentation results, outperforming the previous state-of-the-art in both accuracy and speed. The combination of fast and accurate segmentation is not only beneficial for a posteriori data processing, it also makes online monitoring of thousands of trapped cells or closed-loop optimal experimental design feasible from an image processing perspective. |
Book Title: | 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
Place of Publication: | Darmstadt |
Publisher: | IEEE |
Collation: | 8 Seiten |
Classification DDC: | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
Divisions: | 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems 18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab Interdisziplinäre Forschungsprojekte > Centre for Synthetic Biology |
Event Title: | IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB) |
Event Location: | Online |
Event Dates: | 27.-29.10.2020 |
Date Deposited: | 20 Jul 2022 13:50 |
Last Modified: | 12 Apr 2023 09:42 |
DOI: | 10.26083/tuprints-00021524 |
Corresponding Links: | |
URN: | urn:nbn:de:tuda-tuprints-215242 |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/21524 |
PPN: | 49790943X |
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