Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study
M. Khalid Khan Niazi, Michael Pennell, Camille T. Elkins, Jessica A. Hemminger, Ming Li Jin, Sean Kirby, Habibe Kurt, Barrie Miller, Elizabeth F. Plocharczyk, Rachel M. Roth, Rebecca Ziegler, Arwa Shana’ah, Fred Racke, Gerard Lozanski, Metin N. Gürcan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
Presence of Ki-67, a nuclear protein, is typically used to measure cell proliferation. The quantification of the Ki-67 proliferation index is performed visually by the pathologist; however, this is subject to inter- and intra-reader variability. Automated techniques utilizing digital image analysis by computers have emerged. The large variations in specimen preparation, staining, and imaging as well as true biological heterogeneity of tumor tissue often results in variable intensities in Ki-67 stained images. These variations affect the performance of currently developed methods. To optimize the segmentation of Ki-67 stained cells, one should define a data dependent transformation that will account for these color variations instead of defining a fixed linear transformation to separate different hues. To address these issues in images of tissue stained with Ki-67, we propose a methodology that exploits the intrinsic properties of CIE L∗a∗b∗ color space to translate this complex problem into an automatic entropy based thresholding problem. The developed method was evaluated through two reader studies with pathology residents and expert hematopathologists. Agreement between the proposed method and the expert pathologists was good (CCC = 0.80).