Abstract
Accurate and efficient segmentation of live-cell images is critical in maximizing data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of the non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast-cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labeling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub.
Highlights
S. cerevisiae, hereafter referred to as yeast, is a eukaryotic model organism used to study synthetic gene network development and analysis, as well as other biological processes
Microfluidics-enabled time-lapse fluorescence microscopy allows the study of dynamic cellular processes in a single-cell manner
YeastNet was trained on just training portions of our dataset and was used to demonstrate the ability of this model to generalize to unseen test samples of our datasets and all samples in Yeast Image Toolkit (YIT) datasets 1 and 3
Summary
S. cerevisiae, hereafter referred to as yeast, is a eukaryotic model organism used to study synthetic gene network development and analysis, as well as other biological processes. The quantitative analysis of time-lapse fluorescence microscopy images is a powerful tool for large-scale and single-cell analysis of dynamic and noisy cellular processes, such as gene expression [1,2,3]. Microfluidics-enabled time-lapse fluorescence microscopy allows the study of dynamic cellular processes in a single-cell manner. Automated image capturing of multiple fields of view at high imaging frequencies increases the number of tracked cells and enhances the resolution of the data. It greatly increases the number of images that need to be analyzed from hundreds to tens of thousands. Automated solutions for the problem of cell segmentation are necessary to avoid very time-consuming and error prone manual segmentation
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