Color Correction is Essential for Analysis of Histological Images

Example of inconsistent original images (top), and consistency following color normalization (bottom).  Source: Kothari S, et al. J  Am Med Inform Assoc 2013;20:1099–1108, Figure 3.

Example of inconsistent original images (top), and consistency following color normalization (bottom). Source: Kothari S, et al. J Am Med Inform Assoc 2013;20:1099–1108, Figure 3.

Quality control in microscope images (whether from whole slide scanners or traditional microscopes) is paramount to accurate analysis, especially across studies, imaging devices and repositories.  Color correction (including methods such as color normalization, color standardization and color calibration) has been shown to accommodate for color batch effects, and is especially beneficial for automated analysis and computer-assisted diagnosis.

Most approaches to correcting color variation as a batch artifact are found only in the literature, but commercial solutions are now available, and can be adapted to qualify or even calibrate acquisition devices, as is the proposal by the FDA for digital pathology.

 

How do you ensure that you’re getting the best information from your images?  Share a Comment below.

 

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Comments

I have been wondering why discussions about color consistency and calibration come up frequently on platforms on digital pathology and now also on this forum.
Coming from a routine pathology environment, having worked at different locations over the past 35 years I have not seen a single day without variations in color intensity and temperature in both histochemical ( H&E , retic, Pas,etc ) or IHC. Such variations occur within and between laboratories. Although I fully agree with the statement that procedures to manage color stability from slide to slide and from scanner to scanner would be ideal for all kinds of automatic measurements, I very much doubt that this will ever be achieved. Developments in automatic image analysis that will be based to strict on color stability will not become successfull in the end. Unless we will achieve a controlled pre-analytic tissue treatment and controlled reagent preparation and incubation such an approach will fail.
From clinical chemistry we have learned that each site should refer to local controls for reference values. In that same line every histopathology lab that goes digital should pay attention to local calibration of colors and commercial or home brew algorithms for image analysis should have such local reference colors available.
Perhaps we should also ask ourselves the question to what extend shape and N/C ratio or epithelium/stroma ratio play an even more important role in support by classification algorithms.

Yes, we have heard from numerous researchers that stain quality and consistency is paramount. Although standards may exist for the raw constituent of a stain (e.g. pigment), the Biological Stain Commission has not issued standards regarding color, intensity, etc., thus stain preparations vary by location and supplier. Further, stain conditions constantly change through pigment depletion, dilution, etc.

Image consistency (including color correction) provides a basis not only for analysis and presentation, but for comparing staining processes for quality control. With image consistency and assuming staining quality control, analysis algorithms can be applied across specimen batches, and even studies spread out across years.

Consistency in staining is an evolving area and certainly progress has been made, but no denying that much more to do. The same applies to digital imaging, an area that has been dominated by equipment and tools designed for the consumer market. For science and medicine, we need to continue push for solutions that address the rigor demanded by science. There is no single answer to the challenges in achieving high-quality, consistent and comparable images for histology. However, ignoring new techniques and tools that reduce variability and subjectivity should not be discounted because they don’t provide an all-encompassing solution. Integrating advancements that deliver new controls, standards and ease of use/workflow efficiencies should be embraced as important steps toward the ultimate goal.