Automating proliferation rate estimation from Ki-67 histology images

Heba Z. Al-Lahham; Raja S. Alomari; Hazem Hiary; Vipin Chaudhary

doi:10.1117/12.911009

23 February 2012 Automating proliferation rate estimation from Ki-67 histology images

Heba Z. Al-Lahham, Raja S. Alomari, Hazem Hiary, Vipin Chaudhary

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83152A (2012) https://doi.org/10.1117/12.911009
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Breast cancer is the second cause of women death and the most diagnosed female cancer in the US. Proliferation rate estimation (PRE) is one of the prognostic indicators that guide the treatment protocols and it is clinically performed from Ki-67 histopathology images. Automating PRE substantially increases the efficiency of the pathologists. Moreover, presenting a deterministic and reproducible proliferation rate value is crucial to reduce inter-observer variability. To that end, we propose a fully automated CAD system for PRE from the Ki-67 histopathology images. This CAD system is based on a model of three steps: image pre-processing, image clustering, and nuclei segmentation and counting that are finally followed by PRE. The first step is based on customized color modification and color-space transformation. Then, image pixels are clustered by K-Means depending on the features extracted from the images derived from the first step. Finally, nuclei are segmented and counted using global thresholding, mathematical morphology and connected component analysis. Our experimental results on fifty Ki-67-stained histopathology images show a significant agreement between our CAD's automated PRE and the gold standard's one, where the latter is an average between two observers' estimates. The Paired T-Test, for the automated and manual estimates, shows ρ = 0.86, 0.45, 0.8 for the brown nuclei count, blue nuclei count, and proliferation rate, respectively. Thus, our proposed CAD system is as reliable as the pathologist estimating the proliferation rate. Yet, its estimate is reproducible.

Citation Download Citation

Heba Z. Al-Lahham, Raja S. Alomari, Hazem Hiary, and Vipin Chaudhary "Automating proliferation rate estimation from Ki-67 histology images", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83152A (23 February 2012); https://doi.org/10.1117/12.911009

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