Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning

IEEE Transactions on Biomedical Engineering

Volumn 62, Issue 10, 2015, Pages 2421-2433

  Song, Youyi ^a   Zhang, Ling ^a   Chen, Siping ^a   Ni, Dong ^a   Lei, Baiying ^a   Wang, Tianfu ^a  

^a Shenzhen University School of Medicine   (China)

Author keywords

Cervical segmentation; Coarse to fine; Graph partitioning; Multiscale convolutional network (MSCN); Touchingcell splitting

Indexed keywords

CELLS; CONVOLUTION; CYTOLOGY; GRAPH THEORY; IMAGE SEGMENTATION; PIXELS;

COARSE SEGMENTATION; COARSE TO FINE; CONTEXTUAL INFORMATION; CONVOLUTIONAL NETWORKS; GRAPH PARTITIONING; NUCLEUS SEGMENTATION; SCALE INVARIANT FEATURES; TOUCHINGCELL SPLITTING;

COMPLEX NETWORKS;

CELL NUCLEUS; CYTOPLASM; EXPERIMENTAL MODEL; EXTRACT; LEARNING; CHEMISTRY; CYTOCHEMISTRY; CYTOLOGY; FEMALE; HUMAN; IMAGE PROCESSING; MICROSCOPY; PATHOLOGY; PROCEDURES; UTERINE CERVIX;

CELL NUCLEUS; CERVIX UTERI; CYTOPLASM; FEMALE; HISTOCYTOCHEMISTRY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MICROSCOPY;

EID: 84950238277     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2015.2430895     Document Type: Article

References (51)

1. A. Jemal et al., "Global cancer statistics, " CA-Cancer J. Clin., vol. 61, pp. 69-90, 2011.
2. W. H. O. R. Health, W. H. O. C. Diseases, and H. Promotion, Comprehensive Cervical Cancer Control: A Guide to Essential Practice, World Health Organization, Geneva, Switzerland, 2006.
3. J. Ferlay et al., "Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008, " Int. J. Cancer, vol. 127, pp. 2893-2917, 2010.
4. D. Saslow et al., "American cancer society, American society for colposcopy and cervical pathology, and American society for clinical pathology screening guidelines for the prevention and early detection of cervical cancer, " CA-Cancer J. Clin., vol. 62, pp. 147-172, 2012.
5. G. G. Birdsong, "Automated screening of cervical cytology specimens, " Hum. Pathol., vol. 27, pp. 468-481, 1996.
6. E. Bengtsson, "Computerized cell image analysis: Past, present, and future, " in Image Analysis. New York, NY, USA: Springer, 2003, pp. 395-407.
7. E. Bengtsson, "Recognizing signs of malignancy-The quest for computer assisted cancer screening and diagnosis systems, " in Proc Int. Conf. Comput. Intell., 2010, pp. 1-6.
8. L. Zhang et al., "Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts, " Comput. Med. Imag. Graph., vol. 38, pp. 369-380, 2014.
9. Y.Marinakis et al., "Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification, " Comput. Biol. Med., vol. 39, pp. 69-78, 2009.
10. O. Lezoray and H. Cardot, "Cooperation of color pixel classification schemes and color watershed: A study for microscopic images, " IEEE Trans. Image Process., vol. 11, no. 7, pp. 783-789, Jul. 2002.
11. M.-H. Tsai et al., "Nucleus and cytoplast contour detector of cervical smear image, " Pattern Recogn. Lett., vol. 29, pp. 1441-1453, 2008.
12. A. Gençtav, S. Aksoy, and S. Ö nder, "Unsupervised segmentation and classification of cervical cell images, " Pattern Recog., vol. 45, pp. 4151-4168, 2012.
13. L. Zhang et al., "Automation-assisted cervical cancer screening inmanual liquid-based cytology with hematoxylin and eosin staining, " Cytometry A, vol. 85, no. 3, pp. 214-230, 2014.
14. S.-F. Yang-Mao et al., "Edge enhancement nucleus and cytoplast contour detector of cervical smear images, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 2, pp. 353-366, Apr. 2008.
15. P. Bamford and B.C. Lovell, "Awater immersion algorithm for cytological image segmentation, " in Proc. APRS Image SegmentationWorkshop, 1996, pp. 75-79.
16. K. Li et al., "Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF snake, " Pattern Recog., vol. 45, pp. 1255-1264, 2012.
17. C. Bergmeir et al., "Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework, " Comput. Method. Program. Biomed., vol. 107, pp. 497-512, 2012.
18. M. E. Plissiti et al., "Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering, " IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 2, pp. 233-241, Mar. 2011.
19. M. E. Plissiti et al., "Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images, " Pattern Recog. Lett., vol. 32, pp. 838-853, 2011.
20. H.-S. Wu et al., "A parametric fitting algorithm for segmentation of cell images, " IEEE Trans. Biomed. Eng., vol. 45, no. 3, pp. 400-407, Mar. 1998.
21. P. Bamford and B. Lovell, "Unsupervised cell nucleus segmentation with active contours, " Signal Process., vol. 71, pp. 203-213, 1998.
22. C. Farabet et al., "Learning hierarchical features for scene labeling, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1915-1929, Aug. 2013.
23. B. B. Le Cun et al., "Handwritten digit recognition with a backpropagation network, " in Proc. Neural Inf. Process. Syst., 1990, pp. 396-404.
24. Y. LeCun et al., "Gradient-based learning applied to document recognition, " Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
25. V. Jain et al., "Supervised learning of image restorationwith convolutional networks, " in Proc. Conf. Comput. Vis., 2007, pp. 1-8.
26. B. Pang et al., "Cell nucleus segmentation in color histopathological imagery using convolutional networks, " in Proc. Chin. Conf. Pattern Recog., 2010, pp. 1-5.
27. D. Ciresan et al., "Deep neural networks segment neuronal membranes in electron microscopy images, " in Proc. Neural Inf. Process. Syst., 2012, pp. 2843-2851.
28. S. C. Turaga et al., "Convolutional networks can learn to generate affinity graphs for image segmentation, " Neural Comput., vol. 22, pp. 511-538, 2010.
29. D. Padfield et al., "Spatio-temporal cell cycle phase analysis using level sets and fast marching methods, " Med. Imag. Anal., vol. 13, pp. 143-155, 2009.
30. S. Ali and A. Madabhushi, "An integrated region-, boundary-, shapebased active contour for multiple object overlap resolution in histological imagery, " IEEE Trans. Med. Imag., vol. 31, no. 7, pp. 1448-1460, Jul. 2012.
31. P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient graph-based image segmentation, " Int. J. Comput. Vis., vol. 59, pp. 167-181, 2004.
32. J. Shi and J. Malik, "Normalized cuts and image segmentation, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000.
33. A. Lucchi et al., "Supervoxel-based segmentation of mitochondria in em image stacks with learned shape features, " IEEE Trans. Med. Imag., vol. 31, no. 2, pp. 474-486, Feb. 2012.
34. A. Delong and Y.Boykov, "Globally optimal segmentation ofmulti-region objects, " in Proc. Int. Conf. Comput. Vis., 2009, pp. 285-292.
35. Y. Y. Boykov andM.-P. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in ND images, " in Proc. Int. Conf. Comput. Vis., 2001, pp. 105-112.
36. Y. Boykov and V. Kolmogorov, "An experimental comparison of mincut/max-flow algorithms for energy minimization in vision, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. 1124-1137, Sep. 2004.
37. X. Lou et al., "Learning to segment dense cell nuclei with shape prior, " in Proc. Int. Conf. Comput. Vis. Pattern Recogn., 2012, pp. 1012-1018.
38. H. Chang et al., "Invariant delineation of nuclear architecture in Glioblastoma multiforme for clinical andmolecular association, " IEEE Trans.Med. Imag., vol. 32, no. 4, pp. 670-682, Apr. 2013.
39. J. Cheng and J. C. Rajapakse, "Segmentation of clustered nuclei with shape markers and marking function, " IEEE Trans. Biomed. Eng., vol. 56, no. 3, pp. 741-748, Mar. 2009.
40. H. Yang and N. Ahuja, "Automatic segmentation of granular objects in images: Combining local density clustering and gradient-barrier watershed, " Pattern Recog., vol. 47, pp. 2266-2279, 2014.
41. N. Jamil et al., "A modified edge-based region growing segmentation of geometric objects, " in Visual Informatics: Sustaining Research and Innovations. New York, NY, USA: Springer, 2011, pp. 99-112.
42. M. E. Plissiti and C. Nikou, "Overlapping cell nuclei segmentation using a spatially adaptive active physical model, " IEEE Trans. Image Process., vol. 21, no. 11, pp. 4568-4580, Nov. 2012.
43. C. Park et al., "Segmentation, inference and classification of partially overlapping nanoparticles, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 3, pp. 669-681, Mar. 2013.
44. P. Quelhas et al., "Cell nuclei and cytoplasm joint segmentation using the sliding band filter, " IEEE Trans. Med. Imag., vol. 29, no. 8, pp. 1463-1473, Aug. 2010.
45. H. Kong et al., "Partitioning histopathological images: An integrated framework for supervised color-texture segmentation and cell splitting, " IEEE Trans. Med. Imag., vol. 30, no. 9, pp. 1661-1677, Sep. 2011.
46. H. Fatakdawala et al., "Expectation maximization driven geodesic active contour with overlap resolution (emagacor): Application to lymphocyte segmentation on breast cancer histopathology, " IEEE Trans. Biomed. Eng., vol. 57, no. 7, pp. 1676-1689, Jul. 2010.
47. L. Zhang et al., "Automation-assisted cervical cancer screening inmanual liquid-based cytology with hematoxylin and eosin staining, " Cytometry A, vol. 85, pp. 214-230, 2014.
48. R. Achanta et al., "SLIC superpixels compared to state-of-the-art superpixel methods, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
49. Y. Boykov et al., "Fast approximate energy minimization via graph cuts, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
50. L. R. Dice, "Measures of the amount of ecologic association between species, " Ecology, vol. 26, pp. 297-302, 1945.
51. Y. Al-Kofahi et al., "Improved automatic detection and segmentation of cell nuclei in histopathology images, " IEEE Trans. Biomed. Eng., vol. 57, no. 4, pp. 841-852, Apr. 2010.