Abstract
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
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References
Sjöström, P.J., Frydel, B.R., Wahlberg, L.U.: Artificial neural network-aided image analysis system for cell counting. Cytometry 36(1), 18–26 (1999)
Loukas, C.G., Wilson, G.D., Vojnovic, B., Linney, A.: An image analysis-based approach for automated counting of cancer cell nuclei in tissue sections. Cytometry Part A 55A(1), 30–42 (2003)
Ali, R., Gooding, M., Szilágyi, T., Vojnovic, B., Christlieb, M., Brady, M.: Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images. Machine Vision and Applications 23(4), 607–621 (2012)
Li, K., Chen, M., Kanade, T., Miller, E., Weiss, L., Campbell, P.: Cell population tracking and lineage construction with spatiotemporal context. Medical Image Analysis 12(5), 546–566 (2008)
Long, X., Cleveland, W., Yao, Y.: Automatic detection of unstained viable cells in bright field images using a support vector machine with an improved training procedure. Computers in Biology and Medicine 36(4), 339–362 (2006)
Lulevich, V., Shih, Y.P., Lo, S.H., Liu, G.Y.: Cell tracing dyes significantly change single cell mechanics. The Journal of Physical Chemistry B 113(18), 6511–6519 (2009)
van Opstal, W., Ranger, C., Lejeune, O., Forgez, P., Boudin, H., Bisconte, J., Rostene, W.: Automated image analyzing system for the quantitative study of living cells in culture. Microscopy Research and Technique 28(5), 440–447 (1994)
Long, X., Cleveland, W., Yao, Y.: A new preprocessing approach for cell recognition. IEEE Transactions on Information Technology in Biomedicine 9(3), 407–412 (2005)
Mualla, F., Schöll, S., Sommerfeldt, B., Maier, A., Hornegger, J.: Automatic cell detection in bright-field microscope images using SIFT, random forests, and hierarchical clustering. IEEE Transactions on Medical Imaging 32(12), 2274–2286 (2013)
Nattkemper, T., Ritter, H., Schubert, W.: Extracting patterns of lymphocyte fluorescence from digital microscope images. In: Intelligent Data Analysis in Medicine and Pharmacology, pp. 79–88 (1999)
Mualla, F., Schöll, S., Sommerfeldt, B., Maier, A., Steidl, S., Buchholz, R., Hornegger, J.: Using the low-pass monogenic signal framework for cell/background classification on multiple cell lines in bright-field microscope images. International Journal of Computer Assisted Radiology and Surgery, 1–8 (2013)
Pan, J., Kanade, T., Chen, M.: Learning to detect different types of cells under phase contrast microscopy. In: Microscopic Image Analysis with Applications in Biology (2009)
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect cells using non-overlapping extremal regions. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 348–356. Springer, Heidelberg (2012)
Pan, J., Kanade, T., Chen, M.: Heterogeneous conditional random field: Realizing joint detection and segmentation of cell regions in microscopic images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2940–2947 (2010)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
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Mualla, F. et al. (2014). Unsupervised Unstained Cell Detection by SIFT Keypoint Clustering and Self-labeling Algorithm. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8675. Springer, Cham. https://doi.org/10.1007/978-3-319-10443-0_48
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DOI: https://doi.org/10.1007/978-3-319-10443-0_48
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