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Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning

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,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Prangemeier, Tim ; Wildner, Christian ; Françani, André O. ; Reich, Christoph ; Koeppl, Heinz
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
ISBN: 978-1-7281-9468-4
Collation: 8 Seiten
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab
Interdisziplinäre Forschungsprojekte
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
DOI: 10.26083/tuprints-00021524
URL / URN: https://tuprints.ulb.tu-darmstadt.de/21524
URN: urn:nbn:de:tuda-tuprints-215242
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