Quantitative image analysis in mammary gland biology

Journal of Mammary Gland Biology and Neoplasia

Volumn 9, Issue 4, 2004, Pages 343-359

  Fernandez Gonzalez, Rodrigo ^a   Barcellos Hoff, Mary Helen ^a   Ortiz de Solórzano, Carlos ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

3D reconstruction; Image analysis; Quantification; Segmentation; Whole mount

Indexed keywords

ACCURACY; ARTICLE; BIOLOGICAL MODEL; BREAST CANCER; BREAST CARCINOGENESIS; CELL NUCLEUS; HUMAN; IMAGE ANALYSIS; MAMMARY GLAND FUNCTION; MICROSCOPY; MORPHOLOGY; NONHUMAN; QUANTITATIVE ANALYSIS; THREE DIMENSIONAL IMAGING; TISSUE SECTION; TUMOR VASCULARIZATION; ANIMAL; CYTOLOGY; DIAGNOSTIC IMAGING; METHODOLOGY; PHYSIOLOGY; REVIEW; UDDER;

ANIMALS; CELL NUCLEUS; DIAGNOSTIC IMAGING; HUMANS; MAMMARY GLANDS, ANIMAL;

EID: 22944444900     PISSN: 10833021     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10911-004-1405-9     Document Type: Article

References (69)

1. Cohen C: Image cytometric analysis in pathology. Hum Pathol 2096;27(5):482-93.
2. True LD: Morphometric applications in anatomic pathology. Hum Pathol 2096;27(5):450-67.
3. Russo IH, Russo J: Developmental stage of rat mammary-gland as determinant of it susceptibility to 7,12-dimethylbenz[a]anthracene. J Natl Cancer Inst 2078;61(6):1439-49.
4. Russo J, Russo IH: DNA labeling index and structure of rat mammary-gland as determinants of its susceptibility to carcinogenesis. J Natl Cancer Inst 2078;61(6):1451-9.
5. Cleary MP, Juneja SC, Phillips FC, et al: Leptin receptor-deficient MMTV-TGF-alpha/Lept(db) Lepr(db) female mice do not develop oncogene-induced mammary tumors. Exp Biol Med 2004;229(2):182-93.
6. Bagheri-Yarmand R, Vadlamudi RK, Kumar R: Activating transcription factor 4 overexpression inhibits proliferation and differentiation of mammary epithelium resulting in impaired lactation and accelerated involution. J Biol Chem 2003;278(19):17421-9.
7. De Giovanni C, Nicoletti G, Landuzzi L, et al: Immunoprevention of HER-2/ neu transgenic mammary carcinoma through an interleukin 12-engineered allogeneic cell vaccine. Cancer Res 2004;64(11):4001-9.
8. Westerlind KC, McCarty HL, Gibson KJ, et al: Effect of exercise on the rat mammary gland: Implications for carcinogenesis. Acta Physiol Scand 2002;175(2):147-56.
9. Ip C, Banni S, Angioni E, et al: Conjugated linoleic acid-enriched butter fat alters mammary gland morphogenesis and reduces cancer risk in rats. J Nutr 2099;129(12):2135-42.
10. Ball SM: The development of the terminal end bud in the prepubertal-pubertal mouse mammary gland. Anat Rec 2098;250(4):459-64.
11. Zhu ZJ, Jiang WQ, Thompson HJ: Effect of corticosterone administration on mammary gland development and p27 expression and their relationship to the effects of energy restriction on mammary carcinogenesis. Carcinogenesis 2098;19(12):2101-6.
12. Atwood CS, Hovey RC, Glover JP, et al: Progesterone induces side-branching of the ductal epithelium in the mammary glands of peripubertal mice. J Endocrinol 2000;167(1):39-52.
13. Brown NM, Manzolillo PA, Zhang JX, et al: Prenatal TCDD and predisposition to mammary cancer in the rat. Carcinogenesis 2098;19(9):1623-9.
14. Mehta RG, Moon RC: Effects of 12-O-tetradecanoylphorbol-13-acetate on carcinogen-induced mouse mammary lesions in organ culture. Cancer Res 2086;46(11):5832-5.
15. Hayward SW, Brody JR, Cunha GR: An edgewise look at basal epithelial cells: Three-dimensional views of the rat prostate, mammary gland and salivary gland. Differentiation 2096;60(4):219-27.
16. Hadsell DL, Bonnette S, George J, et al: Diminished milk synthesis in upstream stimulatory factor 2 null mice is associated with decreased circulating oxytocin and decreased mammary gland expression of eukaryotic initiation factors 4E and 4G. Mol Endocrinol 2003;17(11):2251-67.
17. Hilakivi-Clarke L, Clarke R, Onojafe I, et al: A maternal diet high in n-6 polyunsaturated fats alters mammary gland development, puberty onset, and breast cancer risk among female rat offspring. Proc Natl Acad Sci USA 2097;94(17):9372-7.
18. Williams TM, Cheung MWC, Park DS, et al: Loss of caveolin-1 gene expression accelerates the development of dysplastic mammary lesions in tumor-prone transgenic mice. Mol Biol Cell 2003;14(3):1027-42.
19. Wiseman BS, Sternlicht MD, Lund LR, et al: Site-specific inductive and inhibitory activities of MMP-2 and MMP-3 orchestrate mammary gland branching morphogenesis. J Cell Biol 2003;162(6):1123-33.
20. Chang SH, Liu CH, Conway R, et al: Role of prostaglandin E2-dependent angiogenic switch in cyclooxygenase 2-induced breast cancer progression. Proc Natl Acad Sci USA 2004;101(2):591-6.
21. Liu SQ, Weaver DL, Taatjes DJ: Three-dimensional reconstruction by confocal laser scanning microscopy in routine pathologic specimens of benign and malignant lesions of the human breast. Histochem Cell Biol 2097;107(4):267-78.
22. Fernandez-Gonzalez R, Jones A, Garcia-Rodriguez E, et al: System for combined three-dimensional morphological and molecular analysis of thick tissue specimens. Microsc Res Tech 2002;59(6):522-30.
23. Arganda-Carreras I, Fernandez-Gonzalez R, Ortiz de Solorzano C: Automatic registration of serial mammary gland sections. In: Proceedings of the 26th International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, 2004 September 1-5, p. 1691-4.
24. Fernandez-Gonzalez R, Deschamps T, Idica A, et al: Automatic segmentation of histological structure in mammary gland tissue sections. J Biomed Opt 2004;9(3):444-53.
25. Capuco AV, Ellis S, Wood DL, et al: Postnatal mammary ductal growth: three-dimensional imaging of cell proliferation, effects of estrogen treatment, and expression of steroid receptors in prepubertal calves. Tissue Cell 2002;34(3):143-54.
26. Ohtake T, Abe R, Kimijima I, et al: Intraductal extension of primary invasive breast-carcinoma by breast-conservative surgery -computer graphic 3-dimensional reconstruction of mammary duct-alveolar. Cancer 2095;76(1):32-45.
27. Ohtake T, Kimijima I, Fukushima T, et al: Computer-assisted complete three-dimensional reconstruction of the mammary ductal/lobular systems -implications of ductal anastomoses for breast-conserving surgery. Cancer 2001;91(12):2263-72.
28. Going JJ, Moffat DF: Escaping from Flatland: Clinical and biological aspects of human mammary duct anatomy in three dimensions. J Pathol 2004;203(1):538-44.
29. Susnik B, Worth A, Leriche J, et al: Malignancy-associated changes in the breast -changes in chromatin distribution in epithelial-cells in normal-appearing tissue adjacent to carcinoma. Anal Quant Cytol Histol 2095;17(1):62-8.
30. Lockett SJ, Sudar D, Thompson CT, et al: Efficient, interactive, and three-dimensional segmentation of cell nuclei in thick tissue sections. Cytometry 2098;31(4):275-86.
31. Mariuzzi L, Mombello A, Granchelli G, et al: Quantitative study of breast cancer progression: Different pathways for various in situ cancers. Modern Pathol 2002;15(1):18-25.
32. Weyn B, van de Wouwer G, van Daele A, et al: Automated breast tumor diagnosis and grading based on wavelet chromatin texture description. Cytometry 2098;33(1):32-40.
33. Krtolica A, De Solorzano CO, Lockett S, et al: Quantification of epithelial cells in coculture with fibroblasts by fluorescence image analysis. Cytometry 2002;49(2):73-82.
34. Albert R, Muller JG, Kristen P, et al: New method of nuclear grading of tissue sections by means of digital image analysis with prognostic significance for node-negative breast cancer patients. Cytometry 2096;24(2):140-50.
35. Albert R, Muller JG, Kristen P, et al: Objective nuclear grading for node-negative breast cancer patients: Comparison of quasi-3D and 2D image-analysis based on light microscopic images. Lab Invest 2098;78(3):247-59.
36. Lockett SJ, Herman B: Automatic detection of clustered, fluorescent-stained nuclei by digital image-based cytometry. Cytometry 2094;17(1):1-12.
37. Gonzalez RC, Woods RE: Digital image processing. Addison-Wesley; 1992.
38. Wu HS, Barba J, Gil J: Iterative thresholding for segmentation of cells from noisy images. J Microsc (Oxford) 2000;197:296-304.
39. Belien JAM, van Ginkel HAHM, Tekola P, et al: Confocal DNA cytometry: A contour-based segmentation algorithm for automated three-dimensional image segmentation. Cytometry 2002;49(1):12-21.
40. Ridler TW, Calvard S: Picture thresholding using an iterative selection method. IEEE Trans Syst Man Cyber 2078;8(8):630-2.
41. Wu HS, Barba J, Gil J: An iterative algorithm for cell segmentation using short-time Fourier transform. J Microsc (Oxford) 1996;184:127-32.
42. Schnorrenberg F, Tsapatsoulis N, Pattichis CS, et al: Improved detection of breast cancer nuclei using modular neural networks -analysis of nuclei in histopathological sections with a system that closely simulates human experts. IEEE Eng Med Biol Magazine 2000;19(1):48-63.
43. Wu HS, Barba J: An efficient semiautomatic algorithm for cell contour extraction. J Microsc (Oxford) 1995;179:270-6.
44. Einstein AJ, Gil J, Wallenstein S, et al: Reproducibility and accuracy of interactive segmentation procedures for image analysis in cytology. J Microsc (Oxford) 1997;188:136-48.
45. Wu HS, Barba J, Gil J: A parametric fitting algorithm for segmentation of cell images. IEEE Trans Biomed Eng 2098;45(3):400-7.
46. Cong G, Parvin B: Model-based segmentation of nuclei. Pattern Recognition 2000;33(8):1383-93.
47. Yang Q, Parvin B: Harmonic cut and regularized centroid transform for localization of subcellular structures. IEEE Trans Biomed Eng 2003;50(4):469-75.
48. Park CC, Henshall-Powell RL, Erickson AC, et al: Ionizing radiation induces heritable disruption of epithelial cell interactions. Proc Natl Acad Sci USA 2003;100(19):10728-33.
49. Vincent L, Soille P: Wathersheds in digital spaces -an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Machine Intell 2091;13(6):583-98.
50. Belhomme P, Elmoataz A, Herlin P, et al: Generalized region growing operator with optimal scanning: Application to segmentation of breast cancer images. J Microsc (Oxford) 1997;186:41-50.
51. Malpica N, Ortiz de Solorzano C, Vaquero JJ, et al: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 2097;28(4):289-97.
52. Ortiz de Solorzano C, Rodriguez EG, Jones A, et al: Segmentation of confocal microscope images of cell nuclei in thick tissue sections. J Microsc (Oxford) 1999;193:212-26.
53. Chin K, Ortiz de Solorzano C, Knowles D, et al: In situ analyses of genome instability in breast cancer. Nat Genet 2004 (advanced online publication, DOI: 10.1038/ng1409).
54. Wahlby C, Sintorn IM, Erlandsson F, et al: Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J Microsc 2004;215(1):67-76.
55. Ortiz De Solorzano C, Malladi R, Lelievre SA, et al: Segmentation of nuclei and cells using membrane related protein markers. J Microsc (Oxford) 2001;201:404-15.
56. Lee KM, Street WN: A fast and robust approach for automated segmentation of breast cancer nuclei. In: Proceedings of the Second Iasted International Conference on Computer Graphics and Imaging, Palm Springs, CA; 1999. p. 42-7.
57. Lee KM, Street WN: An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei. IEEE Trans Neural Netw 2003;14(3):680-7.
58. Wurflinger T, Stockhausen J, Meyer-Ebrecht D, et al: Robust automatic coregistration, segmentation, and classification of cell nuclei in multimodal cytopathological microscopic images. Comput Med Imaging Graphics 2004;28(1-2):87-98.
59. Welsch CW, Dehoog JV, O'Connor DH, et al: Influence of dietary fat levels on development and hormone responsiveness of the mouse mammary-gland. Cancer Res 2085;45(12):6147-54.
60. Fernandez JL, Goyanes V, LopezFernandez C, et al: Quantification of C-ERB-B-2 gene amplification in breast cancer cells using fluorescence in situ hybridization and digital image analysis. Cancer Genet Cytogenet 2096;86(1):18-21.
61. Klijanienko J, Couturier J, Galut M, et al: Detection and quantitation by fluorescence in situ hybridization (FISH) and image analysis of HER-2/ neu gene amplification in breast cancer fine-needle samples. Cancer Cytopathol 2099;87(5):312-8.
62. Biesterfeld S, Kluppel D, Koch R, et al: Rapid and prognostically valid quantification of immunohistochemical reactions by immunohistometry of the most positive tumour focus. A prospective follow-up study on breast cancer using antibodies against MIB-1, PCNA, ER, and PR. J Pathol 2098;185(1):25-31.
63. Hatanaka Y, Hashizume K, Nitta K, et al: Cytometrical image analysis for immunohistochemical hormone receptor status in breast carcinomas. Pathol Int 2003;53(10):693-9.
64. Lehr HA, Jacobs TW, Yaziji H, et al: Quantitative evaluation of HER-2/ neu status in breast cancer by fluorescence in situ hybridization and by immunohistochemistry with image analysis. Am J Clin Pathol 2001;115(6):814-22.
65. Lehr HA, Hansen KD, Coltrera MD, et al: Photoshop-based image analysis for the semiautomated assessment of Ki-67-defined proliferative activity in the routine diagnosis of breast cancer. Appl Immunohistochem 2096;4(2):117-27.
66. Lehr HA, Mankoff DA, Corwin D, et al: Application of Photoshop-based image analysis to quantification of hormone receptor expression in breast cancer. J Histochem Cytochem 2097;45(11):1559-65.
67. Lehr HA, van der Loos CM, Teeling P, et al: Complete chromogen separation and analysis in double immunohistochemical stains using Photoshop-based image analysis. J Histochem Cytochem 2099;47(1):119-25.
68. Naccarato AG, Viacava P, Bocci G, et al: Definition of the microvascular pattern of the normal human adult mammary gland. J Anat 2003;203(6):599-603.
69. Chantrain CF, DeClerck YA, Groshen S, et al: Computerized quantification of tissue vascularization using high-resolution slide scanning of whole tumor sections. J Histochem Cytochem 2003;51(2):151-8.