Abstract
Cell behavior analysis in high-throughput biological experiments is important for research and discovery in biology and medicine. To perform the high-throughput experiments, it requires to capture images in low frame rate in order to record images on multi-points. In such a low frame rate image sequence, movements of cells between successive frames are often larger than distances to nearby cells, and thus current methods based on proximity do not work properly. In this study, we propose a cell tracking method that enables to track cells in low frame rate by simultaneously estimating all of the cell motions in successive frames. In the experiments under dense conditions in low frame rate, our method outperformed the other methods.
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References
Bensch, R., Ronneberger, O.: Cell segmentation and tracking in phase contrast images using graph cut with asymmetric boundary costs. In: ISBI, pp. 1220–1223 (2015)
Bise, R., Maeda, Y., Kim, M., Kino-oka, M.: Cell tracking under high confluency conditions by candidate cell region detection-based-association approach. In: Biomedical Engineering (2013)
Bise, R., Yin, Z., Kanade, T.: Reliable cell tracking by global data association. In: ISBI, pp. 1004–1010 (2011)
Blackman, S.S.: Multiple-target tracking with radar applications (1986)
Chalfoun, J., Majurski, M., Dima, A., et al.: Lineage mapper: a versatile cell and particle tracker. Sci. Rep. 6(1), 36984 (2016)
Dosovitskiy, A., Fischer, P., et al.: FlowNet: learning optical flow with convolutional networks. In: ICCV (2015)
He, T., Mao, H., Guo, J., Yi, Z.: Cell tracking using deep neural networks with multi-task learning. Image Vis. Comput. 60, 142–153 (2017). Regularization Techniques for High-Dimensional Data Analysis
Kanade, T., Yin, Z., Bise, R., et al.: Cell image analysis: algorithms, system and applications. In: WACV, pp. 374–381 (2011)
Ker, D., Eom, S., Sanami, S., et al.: Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations. Sci. Data 5, 180237 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Schiegg, M., Hanslovsky, P., Kausler, B.X., et al.: Conservation tracking. In: ICCV, pp. 2928–2935 (2013)
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 18H04738 and 18H05104.
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Hayashida, J., Bise, R. (2019). Cell Tracking with Deep Learning for Cell Detection and Motion Estimation in Low-Frame-Rate. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_44
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DOI: https://doi.org/10.1007/978-3-030-32239-7_44
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