Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning
This work addresses a domain-specific problem for researchers in synthetic biology and microscopy by enabling faster and more accurate segmentation, though it is incremental as it applies deep learning to a setting previously reliant on traditional methods.
The paper tackled the problem of segmenting yeast cells in microstructured environments, a bottleneck in microscopy data analysis, by developing convolutional neural networks that outperform previous state-of-the-art methods in both accuracy and speed.
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.