When using different cameras and displays in the shot and display of one subject, different color images are often showed. To solve this problem, we have developed a color chart in which the constituent colors dyed with dyes are evenly distributed in the color space. We have also developed a tool that creates an International Color Consortium (ICC) profile from the captured image of this color chart. In this report, we will describe the color difference correction accuracy when our method is applied to actual stained pathological specimens taken with multiple whole-slide imaging (WSI). We confirmed color correction accuracy of Lab values in major parts such as the cell nucleus. The Lab value of the specimen itself measured by a spectrocolorimeter was compared with that of the captured image. As a result, the color difference ΔE in H&E-stained cell nucleus was improved from 32.2 to 6.4 for Nanozoomer and from 13.5 to 7.2 for ultra-fast scanner (UFS) by our color correction. The results of the evaluations for other areas and other stain methods (PAS, EVG, MT, and PAM) were good. In the future, high-accuracy color correction of teacher data/evaluation data in AI diagnosis using pathological images will be important.
For image devices such as cameras and displays, it is difficult to obtain consistent color reproducibility due to individual manufacturer designs. We propose to improve the color reproducibility of medical images by using color charts. With the spread of COVID-19 infection, the need for telemedicine is increasing from the perspective of infection prevention. In addition, the digitization of medical images will progress in the future, and it is required that the color reproducibility of supervised data at the time of image acquisition be improved for learning / diagnosis assistance by AI. Colors in the specialized field of medical image processing are largely processed in non-standardized methods, raising many challenges in use cases where color is used for diagnostics. Smartphones are used in telemedicine, but color reproducibility may decrease due to differences in lighting as well as equipment, which may lead to a decrease in diagnostic accuracy. We have developed color charts and appropriate image processing algorithms for medical use cases. In the AI accuracy verification in pathological diagnosis, it was also confirmed that the AI diagnosis accuracy can be improved by performing image processing using a color chart.
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