2.5. Machine learning-based segmentation and classification
ilastik was used to distinguish the background from the yeast to be counted. For example, the pixel classification function of the software was used to remove the background of the compressed pictures in batches. The background and yeast samples can be marked many times by different labels under the Training→Live update command in the process of background removal. Regardless of whether the background was simple or complex, it was separated from the yeast to be counted as much as possible (Figure 1C and Fig.SC).
2.6. Establishment and application of ImageJ macro“Yeast Counter”
The ImageJ macro “Yeast Counter” was written in a Java-like programming language, and full operational details are provided in the “Yeast Counter” in the supplementary material. We smoothed and adjusted the threshold of the images processed in batches using ilastik and ImageJ (Image→Adjust→Threshold→Li→Process→Smooth→apply), and subsequently black-and-white images were presented. Then, the Process→Binary→Fill hole and Watershed functions in ImageJ were used to fill the gap (the part marked by the red circle in Figure 1D, Fig.SD) and separate joined cells (the position indicated by the red clipper in Figure 1E, Fig.SE). In addition, Analyse→Analyse Particles → Size, Area, Circ and other indexes can be used to assess the objectives in images. Here, only the Area command was applied to distinguish yeast cells from background fragments (Figure 1F and Fig.SF). Then, the threshold in the Analyse Particles command was set, and the number of yeast cells was counted automatically (Figure 1G and Fig.SG, ”count.ijm” in the supplementary material). The final results were saved as a file in csv format.