![]() ![]() I started off my research with CellProfiler (CP). Table of Illumination Correction Methods Let’s go through each of these methods one by one: CellProfiler In this post, I created the pros and cons based on what I have researched and then my own personal experiences using these methods as seen in Figure 1.įigure 1. This is what makes the retrospective approach much better since you can use it on publically-available data and do not need to make the image acquisition step longer (it’s more convenient). This means that any package or software that uses this approach would be applied at the beginning of the experiment which isn’t always feasible. These images then can be used to derive an illumination correction function. In contrast, a “prospective” approach requires that during the image acquisition stage that a dark image (background without light) and bright image (background with light) must be taken at each site. These methods are all different in their approach and how they are accessed/used.Īll of these use a “retrospective” approach, which can derive an illumination correction function using the images directly. The three methods that I am focusing on are: The data I am using to test these methods are fluorescence microscopy images, specifically Cell Painting. ![]() There are many ways to correct for illumination in multi-cell images, and not all of them will be covered in this post. ![]() Like I said at the start, nothing is “perfect”, but this correction can help to make the results better than using the raw images. finding biomarkers), then the correction of these illumination errors is pertinent for the most accurate results. When running pipelines to find morphology features that distinguish, for example, cells with different genotypes (i.e. Our goal is to minimize the effect of uneven lighting (or other errors like artifacts) on the biology we ultimately want to analyze. If a group of cells are brighter than others in an image, the features of the cells from the brighter group could be interpreted as different from other groups when these cells have close to the same features in reality. texture, size, area, etc.) for each of the cells in an image. One example of a downstream pipeline that is negatively impacted by illumination issues is feature extraction, where software measures different features (i.e. Well, having illumination issues will make further analysis downstream harder or biologically inaccurate. This type of issue, called “vignetting”, requires a computational method that will take the image and change it to where the whole image has even lighting throughout the image.īut why do we care that there is more lighting in one part of the image than the rest? The main issue that IC helps is when the image contains a brighter area in the center that gradually becomes dimmer moving away from the area. Illumination correction (IC) is the method of adjusting the lighting within a collection of images so that the lighting is evenly distributed across the image (no dim or bright spots).ĭepending on the microscope (i.e., sensors) images that are taken of cells/tissues could come with a multitude of issues. What is illumination correction and why is it used? I tested these methods for my current project, with the goal of predicting NF1 genotype from Schwann Cell morphology. Knowing this, I will go into three different methods of illumination correction and give the pros and cons for each. It is really a game of give-and-take when working with image analysis. We have determined that the correct answer to us means that our segmentation method will incorrectly segment a small percent of the cells but correctly segment the majority. In this field, the main goal is to try and minimize any issues within the images that you have.Įxamples of issues include blurry/noisy images, imperfect segmentation, uneven illumination (the main point of this blog), among others.īeing able to interpret if the method you chose worked is up to the scientist’s discretion.īut, one thing to understand is that the concept of a method working or being the correct answer is often unknown, elusive, or flat-out not satisfying.Īs an example, for a project that I am working on with a fellow lab member, we have been struggling with finding the best segmentation method for our data. Illumination Correction: A Comparison of Methodsįor anyone new to cell-image analysis ( like me!), let me preface this blog post with the fact that no matter how good a method is, nothing will ever be “perfect.” ![]()
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