Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Molnar, Csaba ^a   Jermyn, Ian H ^b   Kato, Zoltan ^c   Rahkama, Vesa ^d   Östling, Païvi ^d   Mikkonen, Piia ^d   Pietiaïnen, Vilja ^d   Horvath, Peter ^a,d  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b DURHAM UNIVERSITY   (United Kingdom)

^c J SELYE UNIVERSITY   (Slovakia)

^d UNIVERSITY OF HELSINKI   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

CELL NUCLEUS; HUMAN; HUMAN CELL; IMAGING; MODEL; MORPHOLOGY; STEM CELL CULTURE; ALGORITHM; BIOLOGICAL MODEL; CELL ORGANELLE; FLUORESCENCE MICROSCOPY; METABOLISM; PROCEDURES; REPRODUCIBILITY; SINGLE CELL ANALYSIS; TUMOR CELL LINE;

ALGORITHMS; CELL LINE, TUMOR; CELL NUCLEUS; HUMANS; MICROSCOPY, FLUORESCENCE; MODELS, BIOLOGICAL; ORGANELLES; REPRODUCIBILITY OF RESULTS; SINGLE-CELL ANALYSIS;

EID: 84983804197     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep32412     Document Type: Article

References (39)

1. Moffat, J. G. et al. Phenotypic screening in cancer drug discovery-past, present and future. Nat Rev Drug Discov 13(8), 588-602 (2014).
2. Celli, J. P. et al. An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3d tumour models. Sci Rep 4, 3751 (2014).
3. Singh, S. et al. Increasing the content of high-content screening: An overview. J Biomol Screen 19(5), 640-650 (2014).
4. Uhlmann, V. et al. CP-CHARM: segmentation-free image classification made accessible. BMC Bioinformatics 17(1), 51 (2016).
5. Martin, H. L. et al. High-content, high-throughput screening for the identification of cytotoxic compounds based on cell morphology and cell proliferation markers. PloS One 9(2), e88338 (2014).
6. Hoggar, S. G. Mathematics of digital images: creation, compression, restoration, recognition (Cambridge University Press, 2006).
7. Carpenter, A. E. et al. Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7(10), R100 (2006).
8. Collins, T. J. Imagej for microscopy. Biotechniques 43, 25-30 (2007).
9. Gonzalez, R. C., Woods, R. E. Digital Image Processing (3rd Edition) (Prentice-Hall, 2006).
10. Wang, X. et al. Cell segmentation and tracking using texture-adaptive snakes. In ISBI 2007: Proceedings of 4th IEEE International Symposium on Biomedical Imaging: from nano to macro, 101-104, Metro Washington DC, USA, IEEE (2007).
11. Sommer, C. et al. Ilastik: Interactive Learning and Segmentation Toolkit. In ISBI 2011: Proceedings of 8th IEEE International Symposium on Biomedical Imaging: from nano to macro, 230-233, Chicago, Illinois, USA, IEEE (2011).
12. Linder, N. et al. Identification of tumor epithelium and stroma in tissue microarrays using texture analysis. Diagn Pathol 7 (2012).
13. Turkki, R. et al. Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis. J Clin Pathol (2015).
14. Bamford, P., Lovell, B. Unsupervised cell nucleus segmentation with active contours. Signal Processing 71(2), 203-213 (1998).
15. Linder, N. et al. A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears. PloS One 9(8) (2014).
16. Perrin, G. et al. A marked point process model for tree crown extraction in plantations. ICIP 2005: Proceedings of 12th IEEE International Conference on Image Processing, 709-712, Genova, Italy, IEEE (2005).
17. Thevenaz, P., Unser, M. The snakuscule. In ICIP 2006: Proceedings of 13th IEEE International Conference on Image Processing, 1633-1636, Atlanta, GA, USA, IEEE (2006).
18. Thevenaz, P. et al. The ovuscule. IEEE Trans Pattern Anal Mach Intell 33(2), 382-93 (2011).
19. Olivo-Marin, J.-C. Extraction of spots in biological images using multiscale products. Pattern Recognit 35(9), 1989-1996 (2002).
20. Kimme, C. et al. Finding circles by an array of accumulators. Commun ACM 18(2), 120-122 (1975).
21. Kass, M. et al. Snakes-active contour models. Int J Comput Vis 1(4), 321-331 (1987).
22. Cremers, D. et al. Diffusion snakes: Introducing statistical shape knowledge into the mumford-shah functional.Int J Comput Vis 50(3), 295-313 (2002).
23. Cremers, D. et al. Diffusion-snakes: Combining statistical shape knowledge and image information in a variational framework. In VLSM'01: Proceedings of 1st IEEE Workshop on Variational and Level Set Methods in Computer Vision, 137-44, Vancouver, BC, USA, IEEE (2001).
24. Horvath, P. et al. A higher-order active contour model for tree detection. In ICPR 2006: Proceeding of the 18th International Conference on Pattern Recognition, 130-133 Hong Kong, IEEE (2006).
25. Anastasov, N. et al. A 3d-microtissue-based phenotypic screening of radiation resistant tumor cells with synchronized chemotherapeutic treatment. BMC Cancer 15(1) (2015).
26. Nobis, M. et al. Intravital flim-fret imaging reveals dasatinib-induced spatial control of src in pancreatic cancer. Cancer Res 73(15), 4674-4686 (2013).
27. Hickman, J. A. et al. Three-dimensional models of cancer for pharmacology and cancer cell biology: Capturing tumor complexity in vitro/ex vivo. Biotechnol J 9(9), 1115-1128 (2014).
28. Molnar, C. et al. A Multi-Layer Phase Field Model for Extracting Multiple Near-Circular Objects. In ICPR 2012: Proceedings of 21st International Conference on Pattern Recognition, 1427-1430, Tsukuba, Japan, IEEE (2012).
29. Marques, R. B. et al. High Efficacy of Combination Therapy Using PI3K/AKT Inhibitors with Androgen Deprivation in Prostate Cancer Preclinical Models. Eur Urol 67(6), 1177-1185 (2015).
30. Marques, R. B. et al. The human PC346 xenograft and cell line panel: a model system for prostate cancer progression. The human PC346 xenograft and cell line panel: a model system for prostate cancer progression. Eur Urol 49(2), 245-257 (2006).
31. Smith, K. et al. CIDRE: an illumination-correction method for optical microscopy. Nat Methods 12(5), 404-406 (2015).
32. Rochery, M. et al. Higher order active contours. Int J Comput Vis 69(1), 27-42 (2006).
33. Horvath, P. et al. A higher-order active contour model of a 'gas of circles' and its application to tree crown extraction. Pattern Recognit 42(5), 699-709 (2009).
34. Osher, S., Sethian, J. A. Fronts Propagating with Curvature-Dependent Speed-Algorithms Based on Hamilton-Jacobi Formulations. J Comput Phys 79(1), 12-49 (1988).
35. Rochery, M. et al. Phase field models and higher-order active contours. In ICCV 2005: Proceedings of 10th IEEE International Conference on Computer Vision, 970-976, Beijing, IEEE (2005).
36. Horvath, P., Jermyn, I. H. A 'gas of circles' phase field model and its application to tree crown extraction. In EUSIPCO 2007: Proceedings of 15th European Signal Processing Conference, Poznan, Poland, IEEE (2007).
37. Molnar, C. et al. A New Model for the Segmentation of Multiple, Overlapping, Near-Circular Objects. In DICTA 2015: Proceedings of International Conference on Digital Image Computing: Techniques and Applications, 1-5, Adelaide, Australia, IEEE (2015).
38. Neumann, B. et al. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464(7289), 721-727 (2010).
39. Lehmussola, A. et al. Computational framework for simulating fluorescence microscope images with cell populations. IEEE Trans Med Imaging 26(7), 1010-1016 (2007).