A robust nonlinear tissue-component discrimination method for computational pathology

Laboratory Investigation

Volumn 96, Issue 4, 2016, Pages 450-458

  Sarnecki, Jacob S ^a   Burns, Kathleen H ^b   Wood, Laura D ^b   Waters, Kevin M ^b   Hruban, Ralph H ^b   Wirtz, Denis ^a,b   Wu, Pei Hsun ^a  

^a Johns Hopkins University   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; COLOR; COLOR DECONVOLUTION; COMPUTATIONAL PATHOLOGY; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; IMAGE DISPLAY; IMAGE PROCESSING; IMAGE PROCESSOR; IMAGING SOFTWARE; IMMUNOHISTOCHEMISTRY; LABORATORY AUTOMATION; NONLINEAR TISSUE COMPONENT DISCRIMINATION; PATHOLOGY; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; TISSUE CHARACTERIZATION; TISSUE SECTION; BIOLOGY; COMPUTER ASSISTED DIAGNOSIS; PROCEDURES; REPRODUCIBILITY;

ALGORITHMS; COLOR; COMPUTATIONAL BIOLOGY; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMMUNOHISTOCHEMISTRY; PATHOLOGY, CLINICAL; REPRODUCIBILITY OF RESULTS;

EID: 84962210207     PISSN: 00236837     EISSN: 15300307     Source Type: Journal    
DOI: 10.1038/labinvest.2015.162     Document Type: Article

References (42)

1. Hipp J, Flotte T, Monaco J et al. Computer aided diagnostic tools aim to empower rather than replace pathologists: lessons learned from computational chess. J Pathol Inform 2011;2:25.
2. De Sousa EMF, Wang X, Jansen M et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat Med 2013;19:614-618.
3. Beck AH, Sangoi AR, Leung S et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Trans Med 2011;3:108ra13.
4. Kwak JT, Hewitt SM, Sinha S et al. Multimodal microscopy for automated histologic analysis of prostate cancer. BMC Cancer 2011;11:62.
5. Doyle S, Feldman M, Tomaszewski J et al. A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans Biomed Eng 2012;59:1205-1218.
6. Monaco JP, Tomaszewski JE, Feldman MD et al. High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models. Med Image Anal 2010;14:617-629.
7. Hamilton PW, Wang Y, Boyd C et al. Automated tumor analysis for molecular profiling in lung cancer. Oncotarget 2015;6:27938-27952.
8. Yuan Y, Failmezger H, Rueda OM et al. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci Transl Med 2012;4:157ra143.
9. Veta M, Kornegoor R, Huisman A et al. Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer. Mod Pathol 2012;25:1559-1565.
10. Wang C, Pécot T, Zynger DL et al. Identifying survival associated morphological features of triple negative breast cancer using multiple datasets. J Am Med Inf Assoc 2013;20:680-687.
11. Wang L-W, Qu A-P, Yuan J-P et al. Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value. PLoS One 2013;8:e82314.
12. Dong F, Irshad H, Oh EY et al. Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PLoS One 2014;9:e114885.
13. Cooper LA, Kong J, Gutman DA et al. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images. Lab Invest 2015;95:366-376.
14. Cooper LA, Kong J, Gutman DA et al. Integrated morphologic analysis for the identification and characterization of disease subtypes. J Am Med Inform Assoc 2012;19:317-323.
15. Diaz G, Romero E. Micro-structural tissue analysis for automatic histopathological image annotation. Microsc Res Tech 2012;75: 343-358.
16. Haggerty JM, Wang XN, Dickinson A et al. Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin. BMC Med Imag 2014;14:7.
17. Kothari S, Phan JH, Osunkoya AO et al. In Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine 218-225 (ACM, Orlando, Florida, 2012).
18. Janowczyk A, Chandran S, Singh R et al. High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts. IEEE Trans Biomed Eng 2012;59:1240-1252.
19. Irshad H, Veillard A, Roux L et al. Methods for nuclei detection, segmentation, and classification in digital histopathology: a reviewcurrent status and future potential. IEEE Rev Biomed Eng 2014;7: 97-114.
20. McCann MT, Ozolek JA, Castro CA et al. Automated histology analysis: opportunities for signal processing. IEEE Signal Process Mag 2015;32: 78-87.
21. Falkeholm L, Grant CA, Magnusson A et al. Xylene-free method for histological preparation: a multicentre evaluation. Lab Invest 2001;81: 1213-1221.
22. Martina JD, Simmons C, Jukic DM. High-definition hematoxylin and eosin staining in a transition to digital pathology. J Pathol Inform 2011;2:45.
23. Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 2001;23:291-299.
24. Kothari S, Phan JH, Moffitt RA et al. Automatic batch-invariant color segmentation of histological cancer images, in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pp 657-660, March 30 2011-April 2 2011; doi:10. 1109/ISBI. 2011. 5872492.
25. Pham N-A, Morrison A, Schwock J et al. Quantitative image analysis of immunohistochemical stains using a CMYK color model. Diagn Pathol 2007;2:8.
26. Khan AM, El-Daly H, Rajpoot NM. A Gamma-Gaussian mixture model for detection of mitotic cells in breast cancer histopathology images, in Pattern Recognition (ICPR), 2012 21st International Conference on, pp 149-152, 11-15 November 2012.
27. Veta M, Huisman A, Viergever MA et al. Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, vol., no., pp. 618-621, March 30 2011-April 2 2011; doi:10. 1109/ISBI. 2011. 5872483.
28. Veta M, van Diest PJ, Kornegoor R et al. Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PLoS One 2013;8: e70221.
29. Khan AM, Rajpoot N, Treanor D et al. A non-linear mapping approach to stain normalisation in digital histopathology images using image-specific colour deconvolution. IEEE Trans Biomed Eng 2014;61:6.
30. Hoffman RA, Kothari S, Wang MD. Comparison of normalization algorithms for cross-batch color segmentation of histopathological images. Conf Proc IEEE Eng Med Biol Soc 2014;2014:194-197.
31. Rodic N, Sharma R, Sharma R et al. Long interspersed element-1 protein expression is a hallmark of many human cancers. Am J Pathol 2014;184:1280-1286.
32. Fabbri R, Costa LDF, Torelli JC et al. 2D Euclidean distance transform algorithms: A comparative survey. ACM Comput Surv (CSUR) 2008;40:2.
33. Wienert S, Heim D, Saeger K et al. Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Sci Rep 2012;2:503.
34. Drelie Gelasca E, Obara B, Fedorov D et al. A biosegmentation benchmark for evaluation of bioimage analysis methods. BMC Bioinform 2009;10:368.
35. Otsu N. A threshold selection method from gray-level histograms. Automatica 1975;11:23-27.
36. Powers DMW. Evaluation: from precision, recall and F-meausure to ROC, informedness, markedness & correlation. J Mach Learn Technol 2011;2:37-63.
37. Macenko M, Niethammer M, Marron JS et al. A method for normalizing histology slides for quantitative analysis. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, vols 1 and 2, 2009, pp 1107-1110.
38. Chen JM, Qu AP, Wang LW et al. New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images. Sci Rep 2015;5:10690.
39. Uhlen M, Oksvold P, Fagerberg L et al. Towards a knowledge-based Human Protein Atlas. Nat Biotechnol 2010;28:1248-1250.
40. Bouzin C, Lamba Saini M, Khaing KK et al. Digital pathology: elementary, rapid and reliable automated image analysis. Histopathology 2015; http://onlinelibrary. wiley. com/doi/10. 1111/his. 12867/abstract.
41. Louis DN, Gerber GK, Baron JM et al. Computational pathology: an emerging definition. Arch Pathol Lab Med 2014;138:1133-1138.
42. Roth KA, Almeida JS. Coming into focus: computational pathology as the new big data microscope. Am J Pathol 2015;185:600-601.