New Cividis Colormap Could Enable Color Blind Surgical and Anatomic Pathologists to View Digital Pathology Images Normally
New dichromatic color scale developed by scientists at the Pacific Northwest National Laboratory could play a role in how slides are stained and how software color-codes digital pathology images in ways that make it easier for human eyes to recognize structures and features of interest
Clinical laboratories, anatomic pathologists, and other specialized diagnostics providers play an essential role in precision medicine. Imagine, however, performing surgical pathology analysis on slides using displays that cannot recreate—or worse, inaccurately display—a range of colors used in the image being analyzed.
As many as 8% of men and 0.5% of women of Northern European ancestry already experience issues discerning colors in the interfaces, information, and world around them due to red-green color blindness according to the National Eye Institute. This can lead to potential for misreadings and medical errors.
Now, research from Pacific Northwest National Laboratory (PNNL) holds the potential to establish a standard colormap that eliminates the impact of red-green colorblindness on visuals. Surgical pathologists, for example, spend much of their days viewing slides and/or digital pathology images. Thus, any new method of illustrating/coloring/highlighting features of interest could eventually prove to be a useful innovation in the specialty of anatomic pathology.
In completing their research, the PNNL scientists created an open-source tool called Cmaputil that other researchers can use. Could it enable clinicians and laboratory workers to improve the visibility of critical elements in samples, slides, and other visual data formats used daily at medical laboratories and anatomic pathology groups?
PLOS One published details about the development of the colormap and its potential scientific applications in August.
PNNL’s Cividis Color Scale: A Better Alternative to Rainbow Color Scales?
While the typical rainbow color map draws attention to a chart or image, it is not particularly great at conveying information—especially if the reader is color vision deficient (CVD) or color blind. Yet, despite this, rainbow scales are common in everything from local weather reports and news stories to medical images and medical studies.
Jamie Nuñez, lead author of the PLOS One study and a chemical and biological data analyst at PNNL, told Scientific American, “People like to use rainbow because it catches the eye. But once the eye actually gets there, and people are trying to figure out what’s actually going on inside of the image, that’s kind of where it falls apart.” (Photo copyright: University of Washington.)
PNNL scientists started with the viridis colormap due to “its wide range of colors” and “overall sharpness when overlaid with complex images.” They created an open-source software tool capable of taking existing color scales and simulating the visual effect of red-green color blindness using a mathematical model of human sight. Their software adjusts the scale so that color and brightness vary at a steady rate.
Their adaptions resulted in what they call the “cividis colormap.” It is a blue and yellow scale that provides an accurate change in hue and luminance when compared to changes in the data set. Researchers noted that, to their knowledge, this is the first study to mathematically optimize a colormap specifically for viewing by both those with CVD and those with normal vision.
“Here, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly,” the researchers noted in the PLOS One study.
Example above is of a misleading colormap, taken from the PNNL/PLOS One study. An image of yeast cells is shown in gray scale (left), with a rainbow color scale (middle) and as a person with red-green color blindness sees the rainbow image (right). (Photo/caption copyright: Nuñez JR, Anderton CR, Renslow RS (2018) PLoS ONE 13(7): e0199239/Scientific American.)
The PNNL researchers report that the colormap will soon be ready in a number of tools, including:
According to Scientific American, cividis will be added to the color-scale libraries of roughly a dozen software packages.
“While it may take some time for the full scientific community to both be aware of the need to choose appropriate colormaps and agree on preferred colormaps,” PNNL researchers note, “we hope the code we provide here can help with this transition by allowing others to experiment with the different aspects of colormap design and see how the various characteristics of a colormap affect its interpretation.”
They are concerned that the changing color spaces on future displays may make current colormaps and standards obsolete, as they display colors outside the standard sRGB color space. However, the researchers also note that any change to color spaces could result in an increase in color availability and allow cmaputil to create better-optimized color schemes.
How Cividis and Similar Approaches Might Impact Pathology
While the technology was developed with mass spectrometry and fluid flow analysis in mind, it could prove useful for medical laboratories and specialized diagnostics providers as well—in particular, anatomic pathology and surgical pathology labs.
Coverage of a presentation at the 2011 IEEE Information Visualization Conference by Phys.org highlights a similar concept for diagnosing heart disease. By taking 3D representations of arteries using a rainbow colormap and converting them to 2D projections using a dichromatic black to red colormap, Harvard researchers found their HemoVis tool increased diagnostic accuracy from 39% to 91% in their study.
Technologies and techniques designed for scientific applications often find use in healthcare environments. For anatomic and surgical pathologist and other diagnostics providers, the research from PNNL shows promise for adapting the latest data visualization trends to further improve accuracy, efficiency, and accessibility of medical images, samples, and other complex images used daily in the process of diagnosing disease.
—Jon Stone
Related Information:
End of the Rainbow? New Map Scale Is More Readable by People Who Are Color Blind
Evaluation of Artery Visualizations for Heart Disease Diagnosis
NanoSIMS for Biological Applications: Current Practices and Analyses
New Color Scale Makes Data Visualizations Easier for Colorblind People to Read
The End of the Rainbow? Color Schemes for Improved Data Graphics
Time to Replace ‘Rainbow Color Scale’ for Data Visualization?
How a New Color Scale for Scientific Models Could Improve Healthcare
To Diagnose Heart Disease, Visualization Experts Recommend a Simpler Approach
