Abstract
The automatic analysis of cell mobility has gained increasing relevance given the enormous amount of data that biology researchers have currently to analyze. However, most biology researchers still analyze cells by visual inspection alone, which is time consuming and prone to induce subjective bias. This makes automatic cell’s mobility analysis essential for large scale, objective studies of cells. To evaluate cancer cell’s mobility, biologists establish in vitro assays with cancer cells seeded on native surfaces or on surfaces coated with extracellular matrix components, recording time-lapse brightfield microscopy images. In such analysis only through the use of quantitative automatic analysis tools is it possible to gather evidence to firmly support biological findings.
In order to perform cell mobility analysis, we perform cell tracking based on cell detection. To detect cells with robustness and increased performance we propose the use of a local interest point detector, the scale-normalized Laplacian of Gaussians filter which enhances the image’s blob like structure which corresponds to cell locations. For cell’s mobility analysis the tracking of cells is performed by a detection association approach assuming either a random or a constant velocity motion and using similarity measures as cross correlation coefficient and SIFT descriptors similarity.
Based on experimental results we found that the assumption of a random motion and the use of the SIFT descriptors for the tracking process outperformed all the other approaches obtaining an accuracy in the detection process of 78.6% and considering the tracking, 87.1% of the total number of cell associations between frames were correctly identified.
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References
Quelhas, P., Marcuzzo, M., Mendona, A.M., Oliveira, M.J., Campilho, A.: Cancer cell detection and invasion depth estimation in brightfield images. In: British Machine Vision Conference, pp. 1–10 (2009)
Usaj, M., Torkar, D., Kanduser, M., Miklavcic, D.: Cell counting tool parameters optimization approach for electroporation efficiency determination of attached cells in phase contrast image. Journal of Microscopy 241(3), 303–314 (2010)
Al-Kofahi, O., Radke, R.J., Goderie, S.K., Shen, Q., Temple, S., Roysam, B.: Automated cell lineage construction: a rapid method to analyze clonal development established with murine neural progenitor cells. Cell Cycle 5(3), 327–335 (2006)
Padfield, D., Rittscher, J., Roysam, B.: Spatio-temporal cell segmentation and tracking for automated screening. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2008, pp. 376–379 (2008)
Meijering, E.: Cell segmentation: 50 years down the road (life sciences). IEEE Signal Processing Magazine 29(5), 140–145 (2012)
Esteves, T., Quelhas, P., Mendona, A.M., Campilho, A.: Gradient convergence filters for cell nuclei detection: a comparison study with a phase based approach. Machine Vision and Applications 23(4), 623–638 (2012)
Xiong, G., Zhou, X., Ji, L., Bradley, P., Perrimon, N., Wong, S.: Segmentation of drosophila RNAI fluorescence images using level sets. In: Proc. IEEE International Conference on Image Processing, pp. 73–76 (2006)
Marcuzzo, M., Quelhas, P., Campilho, A., Mendonça, A.M., Campilho, A.: Automated arabidopsis plant root cell segmentation based on svm classification and region merging. Computers in Biology and Medicine 39(9) (2009)
Smith, K., Carleton, A., Lepetit, V.: General constraints for batch multiple-target tracking applied to largescale videomicroscopy. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), vol. 1, pp. 1–8 (2008)
Kachouie, N.N., Fieguth, P., Ramunas, J., Jervis, E.: Probabilistic model-based cell tracking. International Journal of Biomedical Imaging, 1–10 (2006)
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)
Lindeberg, T.: Scale-space theory: A basic tool for analyzing structures at different scales. Journal of Applied Statistics 21(2), 224–270 (1994)
Lewis, J.P.: Fast template matching. In: Vision Interface, pp. 120–123 (1995)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
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Esteves, T., Oliveira, M.J., Quelhas, P. (2013). Cancer Cell Detection and Tracking Based on Local Interest Point Detectors. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_49
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DOI: https://doi.org/10.1007/978-3-642-39094-4_49
Publisher Name: Springer, Berlin, Heidelberg
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