(B) Droplet detection comparison among (i) ImageJ (IJ), (ii) CellProfiler (CP), (iii) Ilastik (Ila), and (iv) QuPath (QP).
#Cellprofiler custom script software#
For the analysis of droplet images, we used the four most popular image analysis software that were selected according to hits in social media (Twitter) and Scopus search (obtained on February 11, 2021) (right). We used a fluorescence microscope to obtain “raw images” of droplets that contained fluorescence producing bacteria (middle). (A) We generated water-in-oil droplets using a flow-focusing microfluidic chip (left). Schematic of droplet generation and image analysis of a single image. Then, we took a deeper look into their workflow and assessed their performance with different key parameters ( Figure 1C). (37) We continued with these four popular software tools and used them to detect droplets on the image dataset previously described by Bartkova et al.
Ilastik uses the concept of supervised machine learning in their workflow, (36) and QuPath has been used as a whole slide image analysis tool. The most popular software are ImageJ, (27) CP, (35) Ilastik, and QuPath, in blue, red, cyan, and green color, respectively. Based on the Scopus and Twitter search (obtained on February 11, 2021), we showed the sum of “tweets” or 160-character max of text from Twitter and the sum of Scopus search in scatter plot ( Figure 1A). Both searches were performed to acquire data from Januto December 31, 2020. For finding the results from Scopus’ repository, we also used the same keyword. (33) To find the popularity, we executed Twint (34) Python script using each of the software’s name as the keyword. (32) We found that social media also give researchers the opportunity to “push” their findings and correlate them to a greater citation. Twitter has been used for research purposes before. Here, we use Twitter and Scopus repositories to find the popularity of the software in the field of image analysis. The most popular software for image analysis are ImageJ (IJ), CellProfiler (CP), Ilastik (Ila), and QuPath (QP). (14) However, these kinds of programs are only commercially available. (25) There are some user-friendly software that may be used for droplet microfluidic image analysis, such as the Zen imaging program (26) and NIS-Elements from NIKON. Most of the published articles in droplet detection use scripted programs, such as Circular Hough Transform in Python programming language, (19) Mathematica, (20,21) Scikit-image in Python, (22) Image Processing Toolbox from MATLAB, (23) OpenCV and Keras in Python, (24) and OpenCV in C++. (17,18) Image-based droplet analysis (IDA) often requires specific skills in programming that are not widely available in non-specialist laboratories. (13) This approach has been used for a wide range of experiments, such as bacterial surveillance of foodborne contamination, (14) screening of specific substrates, (15) single-cell analysis, (16) and detecting viable bacteria or viruses (e.g., SARS-CoV-2). (12) The analysis has been implemented in different types of image data, from single static image up to real-time data, either by bright-field or fluorescence microscopy. Image-based analysis has often been used in droplet microfluidic experiments. In our case, CellProfiler (CP) offers the most user-friendly experience for both single image and batch processing analyses.
The rule-based type of software also has a simpler workflow or pipeline, especially aimed for non-experienced users.
In our experimental setting, we find that the rule-based type of software is better suited for image-based droplet detection. We test and evaluate the software’s (i) ability to detect droplets, (ii) accuracy and precision, and (iii) overall components and supporting material. We select the four most popular software and classify them into rule-based and machine learning-based types after assessing the software’s modules. In order to address the issue, we explore the potential use of standalone freely available software to perform image-based droplet detection. Unfortunately, the analysis of images may require specific tools or programming skills to produce the expected results. For droplet analysis, researchers often use image-based detection techniques. This advancement has delivered novel quantification methods, such as droplet digital polymerase chain reaction (ddPCR) and an antibiotic heteroresistance analysis tool. Droplet microfluidics has revealed innovative strategies in biology and chemistry.
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