JSEA  Vol.8 No.6 , June 2015
Automatic and Manual Proliferation Rate Estimation from Digital Pathology Images
ABSTRACT
Digital pathology is a major revolution in pathology and is changing the clinical routine for pathologists. We work on providing a computer aided diagnosis system that automatically and robustly provides the pathologist with a second opinion for many diagnosis tasks. However, inter-observer variability prevents thorough validation of any proposed technique for any specific problems. In this work, we study the variability and reliability of proliferation rate estimation from digital pathology images for breast cancer proliferation rate estimation. We also study the robustness of our recently proposed method CAD system for PRE estimation. Three statistical significance tests showed that our automated CAD system was as reliable as the expert pathologist in both brown and blue nuclei estimation on a dataset of 100 images.

Cite this paper
Rajab, L. , Al-Lahham, H. , Alomari, R. , Obaidat, F. and Chaudhary, V. (2015) Automatic and Manual Proliferation Rate Estimation from Digital Pathology Images. Journal of Software Engineering and Applications, 8, 269-275. doi: 10.4236/jsea.2015.86027.
References
[1]   Lord, S.J., Lei, W., Craft, P., et al. (2007) A Systematic Review of the Effectiveness of Magnetic Resonance Imaging (MRI) as an Addition to Mammography and Ultrasound in Screening Young Women at High Risk of Breast Cancer. European Journal of Cancer, 43, 1905-1917.
http://dx.doi.org/10.1016/j.ejca.2007.06.007

[2]   Rakha, E.A., Reis-Filho, J.S., Baehner, F., Dabbs, D.J., Decker, T., Eusebi, V., Fox, S.B., Ichihara, S., Jacquemier, J., Lakhani, S.R., Palacios, J., Richardson, A.L., Schnitt, S.J., Schmitt, F.C., Tan, P.H., Tse, G.M., Badve, S. and Ellis, I.O. (2010) Breast Cancer Prognostic Classification in the Molecular Era: The Role of Histological Grade. Breast Cancer Research, 12, 207.

[3]   Alomari, R.S., Allen, R., Sabata, B. and Chaudhary, V. (2009) Localization of Tissues in High-Resolution Digital Anatomic Pathology Images. Proceedings of SPIE, Medical Imaging: Computer-Aided Diagnosis, 7260, Article ID: 726016.

[4]   Beresford, M.J., Wilson, G.D. and Makris, A. (2006) Measuring Proliferation in Breast Cancer: Practicalities and Applications. Breast Cancer Research, 8, 216. http://dx.doi.org/10.1186/bcr1618

[5]   Urruticoechea, S.A., Lan, E. and Dowsett, M. (2005) Proliferation Marker Ki-67 in Early Breast Cancer. Journal of Clinical Oncology, 23, 7212-7220. http://dx.doi.org/10.1200/JCO.2005.07.501

[6]   Alomari, R., Ghosh, S., Chaudhary, V. and Al-Kadi, O. (2012) Local Binary Patterns for Stromal Area Removal in Histology Images. Proceedings of the SPIE, Medical Imaging: Computer Aided Diagnosis, 8315, Article ID: 831524.

[7]   Al-Lahham, H.Z., Alomari, R.S., Hiary, H. and Chaudhary, V. (2012) Automation Proliferation Rate Estimation from Breast Cancer Ki-67 Histology Images. Proceedings of the SPIE, Medical Imaging: Computer-Aided Diagnosis, 8315, 83152A.

[8]   Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M. and Yener, B. (2009) Histopathological Image Analysis: A Review. IEEE Reviews in Biomedical Engineering, 2, 147-171.

[9]   Cheng, H.D., Shan, J., Ju, W., Guo, Y. and Zhang, L. (2010) Automated Breast Cancer Detection and Classification Using Ultrasound Images: A Survey. Pattern Recognition, 43, 299-317.
http://dx.doi.org/10.1016/j.patcog.2009.05.012

[10]   Phukpattaranont, P., Limsiroratana, S. and Boonyaphiphat, P. (2009) Computer-Aided System for Microscopic Images: Application to Breast Cancer Nuclei Counting. International Journal of Applied Biomedical Engineering, 2, 69-74.

[11]   Shao, J. and Wang, H.S. (2002) Sample Correlation Coefficients Based on Survey Data Under Regression Imputation. Journal of the American Statistical Association, 79, 544-552.

[12]   Cann, J., Ellin, J., Kawano, Y., Knight, B., Long, R.E., Sam, A., Machotka, V. and Smith, A. (2013) Validation of Digital Pathology Systems in the Regulated Nonclinical Environment. Digital Pathology Association, Madison.

[13]   Watkins, M.W. and Pacheco, M. (2001) Interobserver Agreement in Behavioral Research: Importance and Calculation. Journal of Behavioral Education, 10, 205-212.
http://dx.doi.org/10.1023/A:1012295615144

[14]   Yelton, A.R., Wildman, B.G. and Erickson, M.M.T. (1977) A Probability-Based Formula for Calculating Interobserver Agreement. Journal of applied behavior Analysis, 10, 123-131.
http://dx.doi.org/10.1901/jaba.1977.10-127

 
 
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