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Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy

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Image Analysis and Recognition (ICIAR 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7325))

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Abstract

In the field of biomedical image analysis, motion tracking and segmentation algorithms are important tools for time-resolved analysis of cell characteristics, events, and tracking. There are many algorithms in everyday use. Nevertheless, most of them is not properly validated as the ground truth (GT), which is a very important tool for the verification of image processing algorithms, is not naturally available. Many algorithms in this field of study are, therefore, validated only manually by an human expert. This is usually difficult, cumbersome and time consuming task, especially when single 3D image or even 3D image sequence is considered.

In this paper, we have proposed a technique that generates time-lapse sequences of fully 3D synthetic image datasets. It includes generating shape, structure, and also motion of selected biological objects. The corresponding GT data is generated as well. The technique is focused on the generation of synthetic objects at various scales. Such datasets can be then processed by selected segmentation or motion tracking algorithms. The results can be compared with the GT and the quality of the applied algorithm can be measured.

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References

  1. Arts, T., Hunter, W.C., Douglas, A., Muijtjens, A.M., Reneman, R.S.: Description of the deformation of the left ventricle by a kinematic model. Journal of Biomechanics 25(10), 1119–1127 (1992)

    Article  Google Scholar 

  2. Aubert-Broche, B., Griffin, M., Pike, G.B., Evans, A.C., Collins, D.L.: Twenty new digital brain phantoms for creation of validation image data bases. IEEE Trans. Med. Imaging 25(11), 1410–1416 (2006)

    Article  Google Scholar 

  3. Baddeley, D., Weiland, Y., Batram, C., Birk, U., Cremer, C.: Model based precision structural measurements on barely resolved objects. Journal of Microscopy 237(1), 70–78 (2010)

    Article  MathSciNet  Google Scholar 

  4. Black, M.J., Anandan, P.: The robust estimation of multiple motions: paramet- ric and piecewise-smooth flow fields. Comput. Vis. Image Underst. 63(1), 75–104 (1996)

    Article  Google Scholar 

  5. Dufour, A., Shinin, V., Tajbakhsh, S., Guillen-Aghion, N., Olivo-Marin, J.C., Zimmer, C.: Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Transactions on Image Processing 14(9), 1396–1410 (2005)

    Article  Google Scholar 

  6. Flores-Tapia, D., Thomas, G., Sabouni, A., Noghanian, S., Pistorius, S.: Breast tumor microwave simulator based on a radar signal model. In: International Symposium on Signal Processing and Information Technology, pp. 17–22 (2006)

    Google Scholar 

  7. Gerencser, A.A., Nicholls, D.G.: Measurement of instantaneous velocity vectors of organelle transport: Mitochondrial transport and bioenergetics in hippocampal neurons. Biophysical Journal 95, 3070–3099 (2008)

    Google Scholar 

  8. Graner, F., Glazier, J.A.: Simulation of biological cell sorting using a two-dimensional extended Potts model. Phys. Rev. Lett. 69(13), 2013–2016 (1992)

    Article  Google Scholar 

  9. Grigoryan, A.M., Hostetter, G., Kallioniemi, O., Dougherty, E.R.: Simulation toolbox for 3D-FISH spot-counting algorithms. Real-Time Imaging 8(3), 203–212 (2002)

    Article  MATH  Google Scholar 

  10. Kratky, O., Porod, G.: Röntgenuntersuchung Gelöster Fadenmoleküle. Recueil des Travaux Chimiques des Pays-Bas-J. of the Royal Netherlands Chemical Society 68(12), 1106–1122 (1949)

    Article  Google Scholar 

  11. Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O.: Computational framework for simulating fluorescence microscope images with cell populations. IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007)

    Article  Google Scholar 

  12. Lin, T., Barron, J.L.: Image reconstruction error for optical flow. In: Vision Interface, pp. 73–80 (1994)

    Google Scholar 

  13. Lockett, S.J., Sudar, D., Thompson, C.T., Pinkel, D., Gray, J.W.: Efficient, interactive, and three-dimensional segmentation of cell nuclei in thick tissue sections. Cytometry 31, 275–286 (1998)

    Article  Google Scholar 

  14. Ma, Y., Kamber, M., Evans, A.C.: 3D simulation of PET brain images using segmented MRI data and positron tomograph characteristics. In: Computerized Medical Imaging and Graphics, vol. 17, pp. 365–371 (1993)

    Google Scholar 

  15. Malm, P., Brun, A., Bengtsson, E.: Papsynth: simulated bright-field images of cervical smears. In: Proceedings of the 2010 IEEE International Conference on Biomedical Imaging: From Nano to Macro, ISBI 2010, pp. 117–120. IEEE Press (2010)

    Google Scholar 

  16. Manders, E.M.M., Hoebe, R., Strackee, J., Vossepoel, A.M., Aten, J.A.: Largest contour segmentation: A tool for the localization of spots in confocal images. Cytometry 23, 15–21 (1996)

    Article  Google Scholar 

  17. Matov, A., Edvall, M.M., Yang, G., Danuser, G.: Optimal-flow minimum-cost correspondence assignment in particle flow tracking. Computer Vision and Image Understanding 115(4), 531–540 (2011)

    Article  Google Scholar 

  18. Moore, J., Drangova, M., Wierzbicki, M., Barron, J.L., Peters, T.: A High Resolution Dynamic Heart Model Based on Averaged MRI Data. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 549–555. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  19. Okamoto, K., Nishio, S., Saga, T., Kobayashi, T.: Standard images for particle-image velocimetry. Measurement Science and Technology 11(6), 685 (2000)

    Article  Google Scholar 

  20. Rabben, S.I., Haukanes, A.L., Irgens, F.: A kinematic model for simulating physiological left ventricular deformation patterns – a tool for evaluation of myocardial strain imaging. In: IEEE Symposium on Ultrasonics, vol. 1, pp. 134–137 (2003) ISBN: 0-7803-7922-5

    Google Scholar 

  21. Rexilius, J., Hahn, H.K., Bourquain, H., Peitgen, H.O.: Ground Truth in MS Lesion Volumetry – A Phantom Study. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 546–553. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  22. Schlaikjer, M., Torp-Pedersen, S., Jensen, J., Stetson, P.: Tissue motion in blood velocity estimation and its simulation. In: Proc. 1998 IEEE Ultrasonics, Symposium, p. 1495 (1998)

    Google Scholar 

  23. Schlaikjer, M., Torp-Pedersen, S., Jensen, J.A.: Simulation of RF data with tissue motion for optimizing stationary echo canceling filters. Ultrasonics 41, 415–419 (2003)

    Article  Google Scholar 

  24. Solórzano, C.O.d., Rodriguez, E.G., Jones, A., Pinkel, D., Gray, J.W., Sudar, D., Lockett, S.J.: Segmentation of confocal microscope images of cell nuclei in thick tissue sections. Journal of Microscopy 193, 212–226 (1999)

    Article  Google Scholar 

  25. Svoboda, D., Homola, O., Stejskal, S.: Generation of 3D Digital Phantoms of Colon Tissue. In: Kamel, M., Campilho, A. (eds.) ICIAR 2011, Part II. LNCS, vol. 6754, pp. 31–39. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  26. Svoboda, D., Kašík, M., Maška, M., Hubený, J., Stejskal, S., Zimmermann, M.: On Simulating 3D Fluorescent Microscope Images. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 309–316. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  27. Svoboda, D., Kozubek, M., Stejskal, S.: Generation of digital phantoms of cell nuclei and simulation of image formation in 3d image cytometry. Cytometry part A 75A(6), 494–509 (2009)

    Article  Google Scholar 

  28. Tavakoli, V., Sahba, N., Ahmadian, A., Alirezaie, J.: An evaluation of different optical flow techniques for myocardial motion analysis in B-Mode echocardiography images. In: 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, IFMBE Proceedings, vol. 21, pp. 506–510. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  29. Tofts, P., Barker, G., Filippi, M., Gawne-Cain, M., Lai, M.: An oblique cylinder contrast-adjusted (OCCA) phantom to measure the accuracy of MRI brain lesion volume estimation schemes in multiple sclerosis. Magnetic Resonance Imaging 15(10), 183–192 (1997)

    Article  Google Scholar 

  30. Ulman, V., Hubený, J.: Pseudo-real Image Sequence Generator for Optical Flow Computations. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 976–985. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  31. Ulman, V.: Fast Anisotropic Filtering and Performance Evaluation Tool for Optical Flow in Biomedical Image Analysis. Ph.D. thesis, Masaryk University (2011)

    Google Scholar 

  32. Waks, E., Prince, J.L., Douglas, A.S.: Cardiac motion simulator for tagged MRI. In: MMBIA 1996: Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 1996), p. 182. IEEE Computer Society, Washington, DC (1996)

    Google Scholar 

  33. Xiong, W., Wang, Y., Ong, S.H., Lim, J.H., Jiang, L.: Learning cell geometry models for cell image simulation: An unbiased approach. In: ICIP, pp. 1897–1900 (2010)

    Google Scholar 

  34. Zhao, T., Murphy, R.F.: Automated learning of generative models for subcellular location: Building blocks for systems biology. Cytometry Part A 71A(12), 978–990 (2007)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Centre for Biomedical Image Analysis, Masaryk University, Brno, 60200, Czech Republic

    David Svoboda & Vladimír Ulman

Authors
  1. David Svoboda
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  2. Vladimír Ulman
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Editors and Affiliations

  1. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

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Svoboda, D., Ulman, V. (2012). Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_56

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  • DOI: https://doi.org/10.1007/978-3-642-31298-4_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31297-7

  • Online ISBN: 978-3-642-31298-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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