Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study

M. Khalid Khan Niazi; Michael Pennell; Camille Elkins; Jessica Hemminger; Ming Jin; Sean Kirby; Habibe Kurt; Barrie Miller; Elizabeth Plocharczyk; Rachel Roth; Rebecca Ziegler; Arwa Shana’ah; Fred Racke; Gerard Lozanski; Metin N. Gurcan

doi:10.1117/12.2007909

29 March 2013 Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study

M. Khalid Khan Niazi, Michael Pennell, Camille Elkins, Jessica Hemminger, Ming Jin, Sean Kirby, Habibe Kurt, Barrie Miller, Elizabeth Plocharczyk, Rachel Roth, Rebecca Ziegler, Arwa Shana’ah, Fred Racke, Gerard Lozanski, Metin N. Gurcan

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Proceedings Volume 8676, Medical Imaging 2013: Digital Pathology; 86760I (2013) https://doi.org/10.1117/12.2007909
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

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).

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M. Khalid Khan Niazi, Michael Pennell, Camille Elkins, Jessica Hemminger, Ming Jin, Sean Kirby, Habibe Kurt, Barrie Miller, Elizabeth Plocharczyk, Rachel Roth, Rebecca Ziegler, Arwa Shana’ah, Fred Racke, Gerard Lozanski, and Metin N. Gurcan "Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study", Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 86760I (29 March 2013); https://doi.org/10.1117/12.2007909

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