We produce abundant fluorescence images in our experiments. However, there are many challenges in the processing of such data, which can be extremely large. Even in the case of 1 hour of imaging for 25 positions at 3 channels every minute, the microscope will yield 4500 high-resolution images. To help with the image processing in such a high throughput, we developed software for microscope image batching(Fig. 1).
(Github: https://github.com/igemsoftware2018/Team_Peking)

This software was written specifically for the problems we encountered.

The first problem we met is that the file names of the exported images are generated according to fuzzy rules. Confusingly, the rules for the number of the positions, channels and times vary among files. To address the diversity of file names of images derived from different microscopes, we managed to build a naming rule manager (Fig. 2), which can create and modify the naming rule. The rules that can be made are flexible, and can contain up to 4 variables and 4 customized strings. Every variable has a customized zero filling function that can solve annoying problems such as the numbers 01 to 99. The users can also share their rules in the form of a file, which may benefit group projects. Please refer to the user manuals for more details.

Figure. 2 The GUI of naming rules manager

After solving the problem of obtaining the files, we can now address their processing.The most important difference between fluorescence microscope images and ordinary photographs is the channel function. We needed to contrast different fluorescence channels to assess the result. However, exporting a great number of large images can be exceedingly time-consuming. Thus, if we can merge the channels or convert the images to grayscale after cropping, we will save much time. Thus, we added the functions of merging(Fig. 3) and grayscale conversion (Fig. 4)to our software first. Please refer to the user manuals for more details.

In living organisms, if we want to show dynamic processes using video recordings, we will need to fuse the images in the correct order to make a video. We therefore wrote functions to export video by time or by z axis.(Fig. 5) Please refer to the user manuals for more details.

In scientific research, cropping is widely used to beautify the images and emphasize the target. To meet the need for flexible cropping, we provided two cropping methods. Drawing cropping can draw the range to be cropped while position cropping crops the given position.(Fig. 6) Please refer to the user manuals for more details. Please refer to the user manuals for more details.