Cell cycle phase classification in 3D in vivo microscopy of Drosophila embryogenesis

BMC Bioinformatics

Volumn 12, Issue SUPPL. 13, 2011, Pages

  Du, Tie H ^a   Puah, Wee C ^a   Wasser, Martin ^a,b  

^a BIOINFORMATICS INSTITUTE   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

2D IMAGES; 3-D IMAGE; AUTOMATED CELL; AUTOMATED IDENTIFICATION; CELL CYCLE; CELL CYCLE PHASE; CELL DIVISIONS; CELL TYPES; CLASSIFICATION PERFORMANCE; CLASSIFIER PERFORMANCE; CULTURED CELL; DEVELOPMENTAL STAGE; DIFFERENT FREQUENCY; DROSOPHILA EMBRYOS; FEATURE REDUCTION; FEATURE SETS; HIGH-CONTENT; IMAGE DATASETS; IMAGE-BASED SCREENINGS; IN-VIVO; LASER SCANNING CONFOCAL MICROSCOPY; LIVE TISSUE; MITOTIC CHROMOSOMES; NUCLEAR DIVISION; PHENOTYPING; SHAPE AND TEXTURES; TIME-LAPSE MICROSCOPY; TRAINING AND TESTING; TEXTURE FEATURES;

ANIMALS; AUTOMATION; BODY FLUIDS; CELL PROLIFERATION; CHROMOSOMES; CLASSIFICATION (OF INFORMATION); CONFOCAL MICROSCOPY; CYTOLOGY; DIAGNOSIS; SUPPORT VECTOR MACHINES; THREE DIMENSIONAL COMPUTER GRAPHICS; TISSUE; BIOINFORMATICS; HISTOLOGY;

THREE DIMENSIONAL;

ANIMALIA;

ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; CELL CYCLE; CELL NUCLEUS; CHROMATIN; CONFOCAL MICROSCOPY; CYTOLOGY; DROSOPHILA; EMBRYO DEVELOPMENT; FLUORESCENCE MICROSCOPY; METABOLISM; PRENATAL DEVELOPMENT; SUPPORT VECTOR MACHINE; THREE DIMENSIONAL IMAGING;

ANIMALS; CELL CYCLE; CELL NUCLEUS; CHROMATIN; DROSOPHILA; EMBRYONIC DEVELOPMENT; IMAGING, THREE-DIMENSIONAL; MICROSCOPY, CONFOCAL; MICROSCOPY, FLUORESCENCE; NEURAL NETWORKS (COMPUTER); SUPPORT VECTOR MACHINES;

EID: 84864072213     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-S13-S18     Document Type: Article

References (23)

1. Chen X, Zhou X, Wong STC. Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans. Biomed. Eng. 2006, 53:762-766. 10.1109/TBME.2006.870201, 16602586.
2. Neumann B, Walter T, Hériché JK, Bulkescher J, Erfle H, Conrad C, Rogers P, Poser I, Held M, Liebel U, Cetin C, Sieckmann F, Pau G, Kabbe R, Wünsche A, Satagopam V, Schmitz MHA, Chapuis C, Gerlich DW, Schneider R, Eils R, Huber W, Peters JM, Hyman AA, Durbin R, Pepperkok R, Ellenberg J. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 2010, 464:721-727. 10.1038/nature08869, 3108885, 20360735.
3. Lu J, Liu T, Yang J. Automated cell phase classification for zebrafish fluorescence microscope images. 20th International Conference on Pattern Recognition 2010, 2584-2587.
4. Zhou X, Li F, Yan J, Wong STC. A novel cell segmentation method and cell phase identification using Markov model. IEEE Trans InfTechnol Biomed 2009, 13:152-157.
5. Wang M, Zhou X, Li F, Huckins J, King RW, Wong STC. Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy. Bioinformatics 2008, 24:94-101. 10.1093/bioinformatics/btm530, 17989093.
6. Wang M, Zhou X, King R, Wong S. Context based mixture model for cell phase identification in automated fluorescence microscopy. BMC Bioinformatics 2007, 8:32. 10.1186/1471-2105-8-32, 1800869, 17263881.
7. Harder N, Mora-Bermúdez F, Godinez WJ, Ellenberg J, Eils R, Rohr K. Automated analysis of the mitotic phases of human cells in 3D fluorescence microscopy image sequences. Med Image Comput Assist Interv 2006, 9:840-848.
8. Huygens. , http://www.svi.nl/HuygensDeconvolution
9. Chinta R, Puah WC, Kriston-Vizi J, Martin W. 3D segmentation for the study of cell cycle progression in live Drosophila embryos. Proceedings of the First International Workshop on Medical Image Analysis and Description for Diagnosis Systems 2009, 43-51. Porto, Portugal.
10. Windreich G, Kiryati N, Lohmann G. Voxel-based surface area estimation: from theory to practice. Pattern Recognition 2003, 36:2531-2541.
11. Herman GT, Zheng J, Bucholtz CA. Shape-Based Interpolation. IEEE Computer Graphics and Applications 1992, 12:69-79.
12. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on 1973, 3:610-621.
13. Haralick RM. Statistical and structural approaches to texture. Proceedings of the IEEE 1979, 67:786- 804.
14. Soh LK, Tsatsoulis C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. Geoscience and Remote Sensing, IEEE Transactions on 1999, 37:780-795.
15. Reunanen J, Guyon I, Elisseeff A. Overfitting in making comparisons between variable selection methods. Journal of Machine Learning Research 2003, 3:1371-1382.
16. Jolliffe IT. Principal Component Analysis. 2002, New York: Springer-Verlag.
17. Duda RO, Hart PE, Stork DG. Pattern Classification. 2000, Wiley-Interscience, 2nd.
18. Blum AL, Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence 1997, 97:245-271.
19. Vapnik V. The Nature of Statistical Learning Theory. 1999, Springer, 2nd.
20. Chih-Chung C, Chih-Jen L. LIBSVM : a library for support vector machines. 2001, Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
21. Yi-Min H, Shu-Xin D. Weighted support vector machine for classification with uneven training class sizes. 2005, 7:4365-4369.
22. van der Maaten LJP. Matlab toolbox for dimensionality reduction. Proceedings of the Belgian-Dutch Artificial Intelligence Conference 2007, 2007:439-440. Utecht, The Netherlands.
23. Fukunaga K, Olsen DR. An algorithm for finding intrinsic dimensionality of data. IEEE Trans. Comput 1971, 20:176-183.