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Yeast cell segmentation in microstructured environments with deep learning

Prangemeier, T. ; Wildner, C. ; Françani, A. ; Reich, C. ; Koeppl, H. (2021)
Yeast cell segmentation in microstructured environments with deep learning.
In: Biosystems
doi: 10.1016/j.biosystems.2021.104557
Article, Bibliographie

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, previously available 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. An U-Net based semantic segmentaiton approach, as well as a direct instance segmentation approach with a Mask R-CNN are demonstrated. 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 methods’ contribution to segmenting yeast in microstructured environments with a typical systems or synthetic biology application. 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: Article
Erschienen: 2021
Creators: Prangemeier, T. ; Wildner, C. ; Françani, A. ; Reich, C. ; Koeppl, H.
Type of entry: Bibliographie
Title: Yeast cell segmentation in microstructured environments with deep learning
Language: English
Date: 9 October 2021
Publisher: Elsevier
Journal or Publication Title: Biosystems
DOI: 10.1016/j.biosystems.2021.104557
URL / URN: https://www.sciencedirect.com/science/article/abs/pii/S03032...
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, previously available 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. An U-Net based semantic segmentaiton approach, as well as a direct instance segmentation approach with a Mask R-CNN are demonstrated. 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 methods’ contribution to segmenting yeast in microstructured environments with a typical systems or synthetic biology application. 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.

Uncontrolled Keywords: Cell segmentation, Systems biology, Synthetic biology, Biomedical image analysis, Microfluidics, Single-cell analysis, Time-lapse, fluorescent microscopy, Deep learning
Additional Information:

Art.No.: 104557

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
Date Deposited: 15 Oct 2021 07:25
Last Modified: 15 Oct 2021 07:25
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