Prangemeier, Tim ; Wildner, C. ; Francani, A. O. ; Reich, C. ; Koeppl, H. (2020):
Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning.
In: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2020),
IEEE, International Conference on Computational Intelligence in Bioinformatics and Computational Biology, virtual Conference, 27.-29.10.2020, ISBN 978-1-7281-9468-4,
DOI: 10.1109/CIBCB48159.2020.9277693,
[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 usecase. 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.

Send an inquiry

Show editorial Details