ORIGINAL ARTICLE
A robust and automated cell counting method in quantification of digital breast cancer immunohistochemistry images
1
Department of Pathology, West China Hospital, Sichuan University
2
Imaging Research Core Facilities, West China Hospital, Sichuan University
Submission date: 2018-03-13
Final revision date: 2018-06-26
Acceptance date: 2018-08-02
Publication date: 2019-12-07
Pol J Pathol 2019;70(3):162-173
KEYWORDS
TOPICS
ABSTRACT
Quantitative analysis of immunohistochemically stained breast cancer specimens by cell counting is important for prognosis and treatment planning. This paper presents a robust, accurate, and novel method to label immunopositive and immunonegative cells automatically. During preprocessing, we developed an adaptive method to correct the colour aberration caused by imaging conditions. Next, a pixel-level segmentation was performed on preprocessed images using a support vector machine with a radial basis function kernel in HSV colour space. The segmentation result was processed by mathematical morphology operations to correct error-segmented regions and extract the marker for each cell. Validation studies showed that the automated cell-counting method had divergences varying from –5.05% to 3.99% compared with manual counting by a pathologist, indicating considerable agreement of the present automated cell counting method with manual counting. Thus, this method can free pathologists from laborious work and can potentially improve the accuracy and the reproducibility of diagnosis.
REFERENCES (34)
1.
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin 2016; 66: 7-30.
2.
Bertucci F, Birnbaum D. Reasons for breast cancer heterogeneity. J Biol 2008; 7: 6.
3.
Mohsin SK, Weiss H, Havighurst T, et al. Progesterone receptor by immunohistochemistry and clinical outcome in breast cancer: a validation study. Mod Pathol 2004; 17: 1545-1554.
4.
Kaufmann M, Pusztai L, Biedenkopf Expert Panel Members. Use of standard markers and incorporation of molecular markers into breast cancer therapy: Consensus recommendations from an International Expert Panel. Cancer 2011; 117: 1575-1582.
5.
de Azambuja E, Cardoso F, de Castro G, et al. Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12,155 patients. Br J Cancer 2007; 96: 1504-1513.
6.
Veronese SM, Gambacorta M, Gottardi O, et al. Proliferation index as a prognostic marker in breast cancer. Cancer 1993; 71: 3926-3931.
7.
Park D, Kåresen R, Noren T, Sauer T. Ki-67 expression in primary breast carcinomas and their axillary lymph node metastases: clinical implications. Virchows Arch 2007; 451: 11-18.
8.
Chung GG, Zerkowski MP, Ghosh S, et al. Quantitative analysis of estrogen receptor heterogeneity in breast cancer. Lab Investig 2007; 87: 662-669.
9.
Levenson RM, Mansfield JR. Multispectral imaging in biology and medicine: Slices of life. Cytom Part A 2006; 69A: 748-758.
10.
Wu Q, Zeng L, Zheng H, Guo N. Precise Segmentation of White Blood Cells by Using Multispectral Imaging Analysis Techniques. 2008 First International Conference on Intelligent Networks and Intelligent Systems. IEEE; 2008; 491-494.
11.
Bell AA, Brauers J, Kaftan JN, et al. High Dynamic Range Microscopy for Cytopathological Cancer Diagnosis. IEEE J Sel Top Signal Process 2009; 3: 170-184.
12.
Bell AA, Meyer-Ebrecht D, Bocking A, Aach T. HDR-Microscopy of Cell Specimens: Imaging and Image Analysis. 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers. IEEE; 2007; 1303-1307.
13.
Ranefall P, Egevad L, Nordin B, Bengtsson E. A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei. Anal Cell Pathol 1997; 15: 145-156.
14.
Loukas CG, Wilson GD, Vojnovic B, Linney A. An image analysis-based approach for automated counting of cancer cell nuclei in tissue sections. Cytometry 2003; 55A: 30-42.
15.
Arambula Cosio F, Marquez Flores JA, Padilla Castaneda MA, et al. Automatic counting of immunocytochemically stained cells. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat No03CH37439). IEEE; 790-793.
16.
Yang X, Li H, Zhou X. Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy. IEEE Trans Circuits Syst I Regul Pap 2006; 53: 2405-2414.
17.
Bewes JM, Suchowerska N, McKenzie DR. Automated cell colony counting and analysis using the circular Hough image transform algorithm (CHiTA). Phys Med Biol 2008; 53: 5991.
18.
Kothari S, Chaudry Q, Wang MD. Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2009; 795-798.
19.
Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images. IEEE Trans Biomed Eng 2010; 57: 841-852.
20.
Grala B, Markiewicz T, Koz³owski W, et al. New automated image analysis method for the assessment of Ki-67 labeling index in meningiomas. Folia Histochem Cytobiol 2009; 47: 587-592.
21.
Connolly C, Fleiss T. A study of efficiency and accuracy in the transformation from RGB to CIELAB color space. IEEE Trans Image Process 1997; 6: 1046-1048.
22.
Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory – COLT ’92. ACM Press, New York 1992; 144-152.
24.
Lee Y, Lin Y, Wahba G. Multicategory Support Vector Machines. J Am Stat Assoc 2004; 99: 67-81.
25.
Scholkopf B, Kah-Kay Sung, Burges CJC, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 1997; 45: 2758-2765.
26.
Haralick RM, Sternberg SR, Zhuang X. Image Analysis Using Mathematical Morphology. IEEE Trans Pattern Anal Mach Intell 1987; PAMI-9: 532-550.
27.
Klingler JW, Vaughan CL, Fraker TD, Andrews LT. Segmentation of echocardiographic images using mathematical morphology. IEEE Trans Biomed Eng 1988; 35: 925-934.
28.
Zana F, Klein J-C. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 2001; 10: 1010-1019.
29.
Zhao Yu-qian, Gui Wei-hua, Chen Zhen-cheng, et al. Medical Images Edge Detection Based on Mathematical Morphology. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE 2005; 6492-6495.
30.
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1: 307-310.
31.
Hammond MEH, Hayes DF, Dowsett M, et al. American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Immunohistochemical Testing of Estrogen and Progesterone Receptors in Breast Cancer. Arch Pathol Lab Med 2010; 134: e48-e72.
32.
Rizzardi AE, Johnson AT, Vogel RI, et al. Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring. Diagn Pathol 2012; 7: 1-10.
33.
Masmoudi H, Hewitt SM, Petrick N, et al. Automated Quantitative Assessment of HER-2/neu Immunohistochemical Expression in Breast Cancer. IEEE Trans Med Imaging 2009; 28: 916-925.
34.
Hall BH, Ianosi-Irimie M, Javidian P, et al. Computer-assisted assessment of the Human Epidermal Growth Factor Receptor 2 immunohistochemical assay in imaged histologic sections using a membrane isolation algorithm and quantitative analysis of positive controls. BMC Med Imaging 2008; 8: 11.
Share
RELATED ARTICLE
| eISSN: | 2084-9869 |
| ISSN: | 1233-9687 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
