Color correction of pathological images *Tokiya Abe,Pinky A. Bautista, Yukako Yagi, John Gilbertson **Yuri Murakami, Masahiro Yamaguchi, Nagaaki Ohyama ***Hideaki Haneishi * Department of Pathology, Harvard Medical School **Imaging Science & Engineering Laboratory, Tokyo Institute of Technology ***Research Center for Frontier Medical Engineering, Chiba University

Why the color of these images is different? Part 1 These images are generated from one H&E slide These differences are due to the different optical characteristic of the 3 imaging systems Imaging System A Imaging System B Imaging System C If a pathologist who is familiar with images of system A, but uses H&E image from system C, he has to calibrate the color of the image in his brain.

These images are generated from one imaging system These differences are due to different staining condition of 3 H&E slides Staining Condition A Staining Condition B Staining Condition C If pathologist uses H&E image with staining condition C, It might be difficult for him to perform accurate diagnosis. Why the color of these images is different? Part 2

Digital Imaging in Pathology Color variability in digital slide images can be stressful for pathologists performing diagnosis. Therefore, color correction for digital slide images is important.

color correction based on 16-band imaging system We have reported color correction of H&E stained images by using 16-band camera*. Although 16 band is ideal, but RGB imaging is widely used…. This color correction method can digitally correct staining condition by adjusting dye amount. 16 bands multispectral microscope system Staining Condition AStaining Condition B (Digital Correction) *T. Abe et al “Color Standardization of pathological images” in 2003 APIII

Objective Investigate the accuracy of color correction for H&E stained images using RGB with respect to 16-band.

Simulation of Color Correction for RGB Simulation of RGB Image H&E stained tissue slide glass slide H&E Stained tissue Investigate the accuracy of color correction using different Wiener Matrices Correction of dye images Generation of Spectral transmittance image Decomposition to dye images Reconstruction of spectral transmittance image Generation of color corrected image Spectral Estimation by Wiener estimation 16 bands Image Acquisition

Experiment Single Wiener matrix for all H&E slides Multiple Wiener matrices corresponding to different staining condition

Spectral Estimation by using Wiener Estimation Wiener Estimation Matrix B GR B GR Spectral transmittance Many types of spectral transmittance Wiener estimation matrix requires many types of spectral transmittance of H&E slide It is difficult to estimate spectral transmittance from RGB signal

H&E Stained Slides with different staining conditions Eosin staining time Five slides of H&E stained liver specimens were prepared under different staining conditions Excess E Over Excess H Normal Hematoxylin staining time Under When “normal” is defined as target slide with optimal staining condition, the color of four slides are corrected into “normal” Normal slide is stained optimally in hospital

Normal and Under stained slide Normal (Target) Under (test) The image labeled as under and normal correspond to tissue slide that are physically stained different staining time. By using color correction method, the color of under stained image can be corrected to have the color of the normal stained image.

Result Single Wiener matrix with RGB it is difficult to see that cytoplasm and RBC are stained with eosin. Single Wiener Matrix Multiple Wiener Matrix RGB16 bands (ideal) RGB Multiple Wiener matrix with RGB it is easy to see that cytoplasm and RBC are stained with eosin. Because their color is not pink. The contrast among nucleus and RBC and cytoplasm is a lot better This result is closer to the ideal result generated from 16 band image

Accuracy of RGB color correction Estimation of Spectral Transmittance Estimation of Amount of DyeColor Correction Single Wiener matrix NRMSE of transmittance NRMSE of dye Color difference Multiple Wiener Matrix The 16-band result is the reference; Graphs represent the average difference between 16-band and RGB for all staining conditions

Conclusion We investigated the accuracy of color correction for H&E stained images using RGB with respect to 16 band With Wiener matrix corresponding to staining condition, the color corrected image by using RGB is close to that one by using 16 bands image in previous method Future Work Automatically selection of Wiener matrix Application of color correction method for Whole slide Image

Acknowledgment We would like to thank CAP !!