Quantitative automated image analysis system with automated debris filtering for the detection of breast carcinoma cells

Acta Cytologica

Volumn 55, Issue 3, 2011, Pages 271-280

  Martin, David T ^a   Sandoval, Sergio ^b   Ta, Casey N ^a   Ruidiaz, Manuel E ^b   Cortes Mateos, Maria Jose ^f   Messmer, Davorka ^f   Kummel, Andrew C ^c   Blair, Sarah L ^d   Wang Rodriguez, Jessica ^e,g  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^g UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Breast conservation therapy; Computer aided diagnosis; Fluorescent microscopy; Imprint cytology

Indexed keywords

ARTICLE; BREAST CARCINOMA; BREAST REDUCTION; CARCINOMA CELL; CELL DENSITY; CLINICAL ARTICLE; CYTOLOGY; EPITHELIUM CELL; HUMAN; HUMAN CELL; IMAGE ANALYSIS; PARTIAL MASTECTOMY; PRIORITY JOURNAL; AUTOMATION; BREAST TUMOR; DIAGNOSTIC IMAGING; FEMALE; IMAGE PROCESSING; INSTRUMENTATION; METHODOLOGY; PATHOLOGY; PROSPECTIVE STUDY; REPRODUCIBILITY;

AUTOMATION; BREAST NEOPLASMS; DIAGNOSTIC IMAGING; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; PROSPECTIVE STUDIES; REPRODUCIBILITY OF RESULTS;

EID: 79960916296     PISSN: 00015547     EISSN: 19382650     Source Type: Journal    
DOI: 10.1159/000324029     Document Type: Article

References (40)

1. Lichter AS, et al: Mastectomy versus breastconserving therapy in the treatment of stage I and II carcinoma of the breast: a randomized trial at the National Cancer Institute. J Clin Oncol 1992; 10: 976-983.
2. Fisher B, et al: Twenty-year follow-up of a randomized trial comparing total mastectomy, lumpectomy, and lumpectomy plus irradiation for the treatment of invasive breast cancer. N Engl J Med 2002; 347: 1233-1241.
3. Fisher B, et al: Reanalysis and results after 12 years of follow-up in a randomized clinical trial comparing total mastectomy with lumpectomy with or without irradiation in the treatment of breast cancer. N Engl J Med 1995; 333: 1456-1461.
4. Carl MM, et al: Ten-year results in 1,070 patients with stages I and II breast cancer treated by conservative surgery and radiation therapy. Cancer 1995; 75: 2328-2336.
5. Funda M, et al: Positive surgical margins and ipsilateral breast tumor recurrence predict disease-specific survival after breast-conserving therapy. Cancer 2003; 97: 926-933.
6. Smitt MC, Horst K: Association of clinical and pathologic variables with lumpectomy surgical margin status after preoperative diagnosis or excisional biopsy of invasive breast cancer. Ann Surg Oncol 2007; 14: 1040-1044.
7. Olson T, et al: Frozen section analysis for intraoperative margin assessment during breast-conserving surgery results in low rates of re-excision and local recurrence. Ann Surg Oncol 2007; 14: 2953-2960.
8. Creager AJ, Shaw JA, Young PR, Geisinger KR: Intraoperative evaluation of lumpectomy margins by imprint cytology with histologic correlation. Arch Pathol Lab Med 2002; 126: 846-848.
9. Muttalib M, et al: Intra-operative assessment of excision margins using breast imprint and scrape cytology. Breast 2005; 14: 42-50.
10. D'Halluin F, et al: Intra-operative touch preparation cytology following lumpectomy for breast cancer: a series of 400 procedures. Breast 2009; 18: 248-253.
11. Cox C, et al: Cytologic evaluation of lumpectomy margins in patients with ductal carcinoma in situ: Clinical outcome. Ann Surg Oncol 1997; 4: 644-649.
12. Klimberg V, Harms S, Korourian S: Assessing margin status. Surg Oncol 1999; 8: 77-84.
13. Cortes-Mateos M, et al: Automated microscopy to evaluate surgical specimens via touch prep in breast cancer. Ann Surg Oncol 2009; 16: 709-720.
14. Anagnostopoulos I, Maglogiannis I: Neural network-based diagnostic and prognostic estimations in breast cancer microscopic instances. Med Biol Eng Comput 2006; 44: 773-784.
15. Boland MV, Markey MK, Murphy RF: Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry 1998; 33: 366-375.
16. Chen W, et al: Image mining for investigative pathology using optimized feature extraction and data fusion. Comput Methods Programs Biomed 2005; 79: 59-72.
17. Mattie ME, et al: PathMaster: content-based cell image retrieval using automated feature extraction. J Am Med Inform Assoc 2000; 7: 404-415.
18. Glotsos D, et al: Automated diagnosis of brain tumours astrocytomas using Probabilistic Neural Network clustering and Support Vector Machines. Int J Neural Syst 2005; 15: 1-11.
19. Lisboa PJ, Taktak AF: The use of artificial neural networks in decision support in cancer: a systematic review. Neural Netw 2006; 19: 408-415.
20. Maglogiannis I, Zafiropoulos E, Anagnostopoulos I: An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl Intell 2009; 30: 24-36.
21. Mango LJ: Computer-assisted cervical cancer screening using neural networks. Cancer Lett 1994; 77: 155-162.
22. Zhou ZH, et al: Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 2002; 24: 25-36.
23. Krishnan MM, et al: Statistical analysis of mammographic features and its classification using support vector machine. Expert Syst Appl 2010; 37: 470-478.
24. Llorà X, Priya A, Bhargava R: Observer-invariant histopathology using genetics-based machine learning. Nat Comput 2009; 8: 101-120.
25. Carpenter A, et al: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 2006; 7:R100.
26. Iwamoto S, et al: The application of direct immunofluorescence to intraoperative neurosurgical diagnosis. Biomol Eng 2000; 17: 17-22.
27. Price JH, Gough DA: Comparison of phasecontrast and fluorescence digital autofocus for scanning microscopy. Cytometry 1994; 16: 283-297.
28. Shen F, Hodgson L, Hahn K: Digital autofocus methods for automated microscopy. Methods Enzymol 2006; 414: 620-632.
29. Sahoo P, Wilkins C, Yeager J: Threshold selection using Renyi's entropy. Pattern Recognit 1997; 30: 71-84.
30. Nodes T, Gallagher N: Median filters: some modifications and their properties. IEEE Trans Acous 1982; 30: 739-746.
31. Weiss B: Fast median and bilateral filtering. ACM Trans Graph 2006; 25: 519-526.
32. Haralick RM, et al: Image analysis using mathematical morphology. IEEE Trans Pattern Anal Mach Intell 1987; 9: 532.
33. Samet H, Tamminen M: Efficient component labeling of images of arbitrary dimension represented by linear bintrees. IEEE Trans Pattern Anal Mach Intell 1988; 10: 579-586.
34. Cooke T, Peake M: The optimal classification using a linear discriminant for two point classes having known mean and covariance. J Multivar Anal 2002; 82: 379-394.
35. Cox CE, et al: Touch preparation cytology of breast lumpectomy margins with histologic correlation. Arch Surg 1991; 126: 490-493.
36. Klimberg VS, Westbrook KC, Korourian S: Use of touch preps for diagnosis and evaluation of surgical margins in breast cancer. Ann Surg Oncol 1998; 5: 220-226.
37. England DW, et al: Assessment of excision margins following wide local excision for breast carcinoma using specimen scrape cytology and tumour bed biopsy. Eur J Surg Oncol 1994; 20: 425-429.
38. Saarela AO, et al: Determinants of positive histologic margins and residual tumor after lumpectomy for early breast cancer: a prospective study with special reference to touch preparation cytology. J Surg Oncol 1997; 66: 248-253.
39. Kriete A, et al: Computer-based cytophotometric classification of thyroid tumors in imprints. J Cancer Res Clin Oncol 1985; 109: 252-256.
40. Gschwind R, et al: Evaluation of shape descriptors for the morphometric analysis of cell nuclei. Pathol Res Pract 1986; 181: 213-222.