Abstract
High-throughput microscopy imaging applications represent an important research field that is focused on testing and comparing lots of different conditions in living systems. It runs over a limited time-frame and per time step images are generated as output; within the time-range a resilient variation in the images of the experiment is characteristic. Studies represent dynamic circumstances expressed in shape variation of the objects under study. For object extraction, i.e. the segmentation of cells, aforementioned conditions have to be taken into account. Segmentation is used to extract objects from images and from objects features are measured. For high-throughput applications generic segmentation algorithms tend to be suboptimal. Therefore, an algorithm is required that can adapt to a range of variations; i.e. self-adaptation of the segmentation parameters without prior knowledge. In order to prevent measurement bias, the algorithm should be able to assess all inconclusive configurations, e.g. cell clusters. The segmentation method must be accurate and robust so that results that can be trustfully used in further analysis and interpretation. For this study a number of algorithms were evaluated and from the results a new algorithm was developed; the watershed masked clustering algorithm. It consists of three steps: (1) a watershed algorithm is used to establish the coarse location of objects, (2) the threshold is optimized by applying a clustering in each watershed region and (3) each mask is reevaluated on consistency and re-optimized so as to result in consistent segmented objects. The evaluation of our algorithm is realized by testing with images containing artificial objects and real-life microscopy images. The result shows that our algorithm is significantly more accurate, more robust and very reproducible.
Keywords
- High-Throughput Imaging
- Segmentation
- Watershed
- Fuzzy C-means clustering
- Fluorescence Microscopy
- Systems Biology
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References
Carpenter, A., Jones, T., Lamprecht, M., Clarke, C., Kang, I., Friman, O., Al, E.: CellProfiler: Image Analysis Software for Identifying and Quantifying Cell Phenotypes. Genome Biology 7(10) (2006)
Pinidiyaarachchi, A., Wählby, C.: Seeded Watersheds for Combined Segmentation and Tracking of Cells. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 336–343. Springer, Heidelberg (2005)
Webb, A.: Statistical Pattern Recognition, 2nd edn. Wiley, UK (2005)
Neumann, B., Held, M., Liebel, U., Erfle, H., Rogers, P., Pepperkok, R., et al.: High-throughput RNAi screening by time-lapse imaging of live human cells. Nature Methods 3(5), 385–390 (2006)
Neumann, B., Walter, T., Hériché, J.K., Bulkescher, J., Erfle, H., Conrad, C., et al.: Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464(7289), 721–727 (2010)
Huang, C., Rajfur, Z., Borchers, C., Schaller, M., Jacobson, K.: JNK Phosphorylates paxillin and Regulates Cell Migration. Nature 424, 219–223 (2003)
van Rijsbergen, C.: Information Retrieval. Butterworth-Heinemann, UK (1979)
Altman, D.G., Bland, J.M.: Statistics Notes: Diagnostic Tests 1: Sensitivity and Specificity. BMJ 308(1552) (1994)
Hancock, E., Kittler, J.: Adaptive Estimation of Hysteresis Thresholds. In: Proc. of Computer Vision and Pattern Recognition, pp. 196–201 (1991)
Verbeek, F.J.: Three Dimensional Reconstruction from Serial Sections Including Deformation correction. PhD Thesis, Delft University of Technology, The Netherlands (1995)
Verbeek, F.J.: Theory & Practice of 3D-reconstructions From serial Sections. In: Baldock, R.A., Graham, J. (eds.) Image Processing, A Practical Approach, pp. 153–195. Oxford University Press, Oxford (1999)
Angulo, J., Schaack, B.: Morphological-Based Adaptive Segmentation and Quantification of Cell Assays in High Content Screening. In: Proc. of the 5th IEEE International Symposium on Biomedial Imaging, pp. 360–363 (2008)
Bernsen, J.: Dynamic Thresholding of Grey-Level Images. In: Proc. of the 8th Int. Conf. on Pattern Recognition (1986)
Roerdink, J.B., Meijster, A.: The Watershed Transform: Definitions, Algorithms and Parallelization Strategies. Fundamenta Informatica, 187–228 (2000)
Fan, J., Han, M., Wang, J.: Single Point Iterative Weighted Fuzzy C-means Clustering Algorithm for Remote Sensing Image Segmentation. Pattern Recognition 42(11), 2527–2540 (2009)
Moffat, J., Grueneberg, D.A., Yang, X., Kim, S.Y., Kloepfer, A.M., Hinkle, G.: A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124(6), 1283–1298 (2006)
Pu, J., McCaig, C.D., Cao, L., Zhao, Z., Segall, J.E., Zhao, M.: EGF receptor Signaling is Essential for Electric-field-directed Migration of Breast Cancer Cells. Journal of Cell Science 120(19), 3395–3403 (2007)
Yan, K., Bertens, L., Verbeek, F.J.: Image Registration and Realignment using Evolutionary Algorithms with High resolution 3D model from Human Liver. In: Proc. CGIM 2010 (2010)
Yan, K., Le Dévédec, S., van de Water, B., Verbeek, F.J.: Cell Tracking and Data Analysis of in vitro Tumour Cells from Time-Lapse Image Sequences. In: Proc. VISAPP 2009, pp. 281–287 (2009)
Damiano, L., Le Dévédec, S.E., Di Stefano, P., Repetto, D., Lalai, R., Truong, H., Xiong, J.L., Danen, E.H., Yan, K., Verbeek, F.J., Attanasio, F., Buccione, R., van de Water, B., Defilippi, P.: p140Cap Suppresses the Invasive Properties of Highly Metastatic MTLn3-EGFR Cells via Paired Cortactin Phosphorylation. Oncogene 30(2) (2011) (in Press)
Ma, L., Staunton, R.: A modied fuzzy C-means image segmentation algorithm for use with uneven illumination patterns. Pattern Recognition 40(11), 3005–3011 (2007)
Sezgin, M., Sankur, B.: Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. Journal of Electronic Imaging 13(1), 146–165 (2004)
Otsu, N.: A Threshold Selection Method from Gray-level Histogram. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)
Venkateswarlua, N., Raju, P.: Fast Isodata Clustering Algorithms. Pattern Recognition 25(3), 335–342 (1992)
van der Putten, P., Bertens, L., Liu, J., Hagen, F., Boekhout, T., Verbeek, F.J.: Classification of Yeast Cells from Image Features to Evaluate. Pathogen Conditions. In: SPIE 6506, MultiMedia Content Access: Algorithms & Systems, vol. 6506, pp. 65060I-1–65060I-14 (2007)
Goldman, R., Swedlow, J., Spector, D.: Live Cell Imaging: A Laboratory Manual. Cold Spring Harbor Laboratory Press, USA (2005)
Medina-Carnicer, R., Madrid-Cuevas, F., Carmona-Poyato, A., Muñoz Salinas, R.: On candidates selection for hysteresis thresholds in edge detection. Pattern Recognition 42(7), 1284–1296 (2008)
Pepperkok, R., Ellenberg, J.: High-throughput Fluorescence Microscopy for Systems Biology. Nature Reviews Molecular Cell Biology 7(9), 690–696 (2006)
Inoue, S.: Video Microscopy: the Fundamentals, 2nd edn. Springer, USA (1997)
Osher, S.J., Fedkiw, R.P.: Level Set Methods and Dynamic Implicit Surfaces. Springer, USA (2002)
LeDévédec, S., Yan, K., de Bont, H., Ghotra, V., Truong, H., Danen, E., Verbeek, F.J., van de Water, B.: A Systems Microscopy Approach to Understand Cancer Cell Migration and Metastasis. Cellular and Molecular in Life Science 67(19), 3219–3240 (2011)
Collins, T.: Image J for microscopy. Bio Techniques 43, 25–30 (2007)
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Yan, K., Verbeek, F.J. (2012). Segmentation for High-Throughput Image Analysis: Watershed Masked Clustering. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Applications and Case Studies. ISoLA 2012. Lecture Notes in Computer Science, vol 7610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34032-1_4
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DOI: https://doi.org/10.1007/978-3-642-34032-1_4
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