Abstract
Cell detection in microscopy images is an important step in the automation of cell based-experiments. We propose a machine learning-based cell detection method applicable to different modalities. The method consists of three steps: first, a set of candidate cell-like regions is identified. Then, each candidate region is evaluated using a statistical model of the cell appearance. Finally, dynamic programming picks a set of non-overlapping regions that match the model. The cell model requires few images with simple dot annotation for training and can be learned within a structured SVM framework. In the reported experiments, state-of-the-art cell detection accuracy is achieved for H&E-stained histology, fluorescence, and phase-contrast images.
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Bernardis, E., Yu, S.X.: Pop out many small structures from a very large microscopic image. Med. Image Anal. 15(5), 690–707 (2011)
Cheng, J., Veronika, M., Rajapakse, J.: Identifying Cells in Histopathological Images. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 244–252. Springer, Heidelberg (2010)
Graf, F., Grzegorzek, M., Paulus, D.: Counting Lymphocytes in Histopathology Images Using Connected Components. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 263–269. Springer, Heidelberg (2010)
Gurcan, M.N., Madabhushi, A., Rajpoot, N.: Pattern Recognition in Histopathological Images: An ICPR 2010 Contest. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 226–234. Springer, Heidelberg (2010)
Joachims, T., Finley, T., Yu, C.N.: Cutting-plane training of structural SVMs. Mach. Learn. 77, 27–59 (2009)
Kuse, M., Khan, M., Rajpoot, N., Kalasannavar, V., Wang, Y.F.: Local isotropic phase symmetry measure for detection of beta cells and lymphocytes. J. Pathol. Inform. 2(2), 2 (2011)
Kuse, M., Sharma, T., Gupta, S.: A Classification Scheme for Lymphocyte Segmentation in H&E Stained Histology Images. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 235–243. Springer, Heidelberg (2010)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vision Comput. 22(10), 761–767 (2004)
Nath, S., Palaniappan, K., Bunyak, F.: Cell Segmentation Using Coupled Level Sets and Graph-Vertex Coloring. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006, Part I. LNCS, vol. 4190, pp. 101–108. Springer, Heidelberg (2006)
Panagiotakis, C., Ramasso, E., Tziritas, G.: Lymphocyte Segmentation Using the Transferable Belief Model. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 253–262. Springer, Heidelberg (2010)
Pearl, J.: Probabilistic reasoning in intelligent systems. Morgan Kaufmann (1988)
Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML 2004, p. 104. ACM (2004)
Vedaldi, A.: A MATLAB wrapper of SVM\(^{\mathrm struct}\) (2011), http://www.vlfeat.org/~vedaldi/code/svm-struct-matlab.html
Vedaldi, A., Fulkerson, B.: VLFeat (2010), http://www.vlfeat.org/
Yin, Z., Bise, R., Chen, M., Kanade, T.: Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers. In: ISBI 2010, pp. 125–128 (2010)
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Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A. (2012). Learning to Detect Cells Using Non-overlapping Extremal Regions. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_43
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DOI: https://doi.org/10.1007/978-3-642-33415-3_43
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