Quantitative evaluation of color difference between actual specimens and whole-slide imaging-scanned images calibrated with commercial color charts

Yusuke Murayama; Tohru Sugiyama; Yoshihiko Ogino; Hiroki Furuta; Takaaki Kambara; Koi Kamisono; Tokiya Abe; Katsura Emoto; Akinori Hashiguchi; Yoichi Kajimura; Michiie Sakamoto

doi:10.1117/12.2611413

4 April 2022 Quantitative evaluation of color difference between actual specimens and whole-slide imaging-scanned images calibrated with commercial color charts

Yusuke Murayama, Tohru Sugiyama, Yoshihiko Ogino, Hiroki Furuta, Takaaki Kambara, Koi Kamisono, Tokiya Abe, Katsura Emoto, Akinori Hashiguchi, Yoichi Kajimura, Michiie Sakamoto

Author Affiliations +

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120391A (2022) https://doi.org/10.1117/12.2611413
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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.

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Yusuke Murayama, Tohru Sugiyama, Yoshihiko Ogino, Hiroki Furuta, Takaaki Kambara, Koi Kamisono, Tokiya Abe, Katsura Emoto, Akinori Hashiguchi, Yoichi Kajimura, and Michiie Sakamoto "Quantitative evaluation of color difference between actual specimens and whole-slide imaging-scanned images calibrated with commercial color charts", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391A (4 April 2022); https://doi.org/10.1117/12.2611413

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