TY - GEN
T1 - Quantitative evaluation of color difference between actual specimens and whole-slide imaging-scanned images calibrated with commercial color charts
AU - Murayama, Yusuke
AU - Sugiyama, Tohru
AU - Ogino, Yoshihiko
AU - Furuta, Hiroki
AU - Kambara, Takaaki
AU - Kamisono, Koi
AU - Abe, Tokiya
AU - Emoto, Katsura
AU - Hashiguchi, Akinori
AU - Kajimura, Yoichi
AU - Sakamoto, Michiie
N1 - Funding Information:
This work was supported by Council for Science, Technology and Innovation, Cross-ministerial Strategic Innovation Promotion Program (SIP), "Innovative AI Hospital System" (Funding Agency: National Institute of Biomedical Innovation, Health and Nutrition (NIBIOHN)).
Publisher Copyright:
© COPYRIGHT SPIE.
PY - 2022
Y1 - 2022
N2 - 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 ?E 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.
AB - 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 ?E 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.
KW - HE stain
KW - WSI
KW - color calibration
KW - color chart
KW - digital pathological image
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U2 - 10.1117/12.2611413
DO - 10.1117/12.2611413
M3 - Conference contribution
AN - SCOPUS:85132828660
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2022
A2 - Tomaszewski, John E.
A2 - Ward, Aaron D.
A2 - Levenson, Richard M.
PB - SPIE
T2 - Medical Imaging 2022: Digital and Computational Pathology
Y2 - 21 March 2022 through 27 March 2022
ER -