Automated, Quantitative Analysis of Histopathological Staining in Nuclei

Technological advances have allowed the generation of high-throughput imaging of tissue sections. However, the analysis of these samples is typically still performed manually by one or multiple pathologists. We present a novel statistical model for the automated, quantitative analysis of these images. Our approach requires minimal tuning and allows recapitulation of estimates of staining strength in the nuclei of tumor cells as estimated by the gold standard. Besides, it compares favorably to other quantitative approaches available in the public domain.

[1]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[2]  David B. Dunson,et al.  A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation , 2009, NIPS.

[3]  J. Pitman.  Combinatorial Stochastic Processes , 2006 .

[4]  Michael I. Jordan,et al.  Hierarchical Dirichlet Processes , 2006 .

[5]  K. Hess,et al.  Automated quantification of apoptosis after neoadjuvant chemotherapy for breast cancer: early assessment predicts clinical response. , 2003, Clinical cancer research : an official journal of the American Association for Cancer Research.

[6]  Soumya Ghosh,et al.  Spatial distance dependent Chinese restaurant processes for image segmentation , 2011, NIPS.

[7]  Hanchuan Peng,et al.  Bioimage informatics: a new area of engineering biology , 2008, Bioinform..

[8]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  École d'été de probabilités de Saint-Flour,et al.  École d'été de probabilités de Saint-Flour XIII - 1983 , 1985 .

[11]  H A Lehr,et al.  Quantitative evaluation of HER-2/neu status in breast cancer by fluorescence in situ hybridization and by immunohistochemistry with image analysis. , 2001, American journal of clinical pathology.

[12]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  D. Aldous.  Exchangeability and related topics , 1985 .

[14]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[16]  M Masseroli,et al.  Automatic quantification of liver fibrosis: design and validation of a new image analysis method: comparison with semi-quantitative indexes of fibrosis. , 2000, Journal of hepatology.