مجله ماشین بینایی و پردازش تصویر

مجله ماشین بینایی و پردازش تصویر

ردیابی خودکار سلول‌ها درتصاویر میکروسکوپی گذر-زمانی

نوع مقاله : مقاله پژوهشی

نویسندگان
گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه بوعلی سینا، همدان، ایران
چکیده
ردیابی سلول‌ها در طول زمان یکی از موثرترین انواع مطالعه برای درک مکانیسم‌های رشد، ترمیم بافت و درمان بیماری در موجودات زنده است. هدف از این تحقیق ارائه روشی جدید با کمک یادگیری ماشینی برای ردیابی سلول‌ها به شکل خودکار در تصاویر میکروسکوپی گذر-زمانی است. با توجه به پیشرفت‌های چشمگیر در فناوری میکروسکوپ در سال‌های اخیر، از میکروسکوپ گذر-زمانی معمولاً برای مطالعه سلول‌های درون اندام‌ها استفاده می‌شود که امکان تجزیه و تحلیل مستقیم رفتارهای سلولی را فراهم می‌کند. در این روش برای ردیابی سلول‌ها، پس از قطعه‌بندی تصاویر با یادگیری عمیق، عمل تشخیص رویداد تقسیم سلولی را با تئوری منحنی کاسینی و عمل پیگرد سلول‌ها در فریم‌های متوالی را با روش تطبیق قالب محدود شده انجام دادیم. عملکرد ردیاب پیشنهادی توسط معیار TRA که یک متریک استاندارد برای ارزیابی الگوریتم‌های ردیابی سلول‌ها است، اندازه‌گیری شد. برای مجموعه داده‌های DIC-C2DH-HeLa و Fluo-N2DH-SIM+ این مقدار به ترتیب برابر 87.5% و 95.3% بدست آمد. یافته‌های مطالعه حاضر نشان می‌دهد که الگوریتم طراحی شده برای ردیابی سلول از دقت قابل قبولی برخوردار است.
کلیدواژه‌ها

   [1]      Maška, M., Ulman, V., Delgado-Rodriguez, P. et al. The Cell Tracking Challenge: 10 years of objective benchmarking. Nature Methods 20, 1010–1020 (2023). https://doi.org/10.1038/s41592-023-01879-y
   [2]      Ulman, V., Maska, M., Magnusson, K.E.G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jalden, J., Blau, H.M., Dzyubachyk, O., Lelieveldt, B.P.F., Xiao, P., Li, Y., Cho, S.-Y., Dufour, A.C., Olivo-Marin, J.C., Reyes-Aldasoro, C.C., Solis-Lemus, J.A., Bensch, R., Brox, T., Stegmaier, J., Mikut, R., Wolf, S., Hamprecht, F.A., Esteves, T., Quelhas, P., Demirel, O.B., Malmstrom, L., Jug, F., Toman¸cak, P., Meijering, E.H.W., Munoz-Barrutia, A., Kozubek, M., Ortiz-de-Solorzano, C.: An objective comparison of cell tracking algorithms. Nature methods 14, 1141–1152 (2017)
   [3]      Martin Maška, Vladimír Ulman, David Svoboda, Pavel Matula, Petr Matula, Cristina Ederra, Ainhoa Urbiola, Tomás España, Subramanian Venkatesan, Deepak M.W. Balak, Pavel Karas, Tereza Bolcková, Markéta Štreitová, Craig Carthel, Stefano Coraluppi, Nathalie Harder, Karl Rohr, Klas E. G. Magnusson, Joakim Jaldén, Helen M. Blau, Oleh Dzyubachyk, Pavel Křížek, Guy M. Hagen, David Pastor-Escuredo, Daniel Jimenez-Carretero, Maria J. Ledesma-Carbayo, Arrate Muñoz-Barrutia, Erik Meijering, Michal Kozubek, Carlos Ortiz-de-Solorzano, A benchmark for comparison of cell tracking algorithms, Bioinformatics, Volume 30, Issue 11, June 2014, Pages 1609–1617, https://doi.org/10.1093/bioinformatics/btu080
   [4]      Ong, J.Y., Torres, J.Z.: Dissecting the mechanisms of cell division. The Journal of Biological Chemistry 294, 11382–11390 (2019). https://doi. org/10.1074/jbc.AW119.008149
   [5]      Li, X., Miao, Y., Pal, D., Devreotes, P.: Excitable networks controlling cell migration during development and disease. Seminars in Cell and Developmental Biology 100, 133–142 (2020) https://doi.org/10.1016/j.semcdb.2019.11.001
   [6]      Freitas, J.T., Jozic, I., Bedogni, B.: Wound healing assay for melanoma cell migration. Methods in molecular biology 2265, 65–71 (2021)
   [7]      Liu, J.C., Zacksenhouse, M., Eisen, A., Nofech-Mozes, S., Zacksen-haus, E.: Identification of cell proliferation, immune response and cell migration as critical pathways in a prognostic signature for her2+:erα-breast cancer. PLoS ONE 12(6), 0179223 (2017) https://doi.org/10.1371/journal.pone.0179223
   [8]      Anjum, S., Gurari, D.: Ctmc: Cell tracking with mitosis detection dataset challenge. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 4228–4237 (2020). https://doi.org/10.1109/CVPRW50498.2020.00499
   [9]      Amat, F., Lemon, W.C., Mossing, D.P., McDole, K., Wan, Y., Branson, K., Myers, E.W., Keller, P.J.: Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nature Methods 11, 951–958 (2014)
[10]      Chen, X., Zhou, X., Wong, S.T.C.: Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Transactions on Biomedical Engineering 53, 762–766 (2006)
[11]      Kok, R.N.U., Hebert, L., Huelsz-Prince, G., Goos, Y.J., Zheng, X., Bozek, K., Stephens, G.J., Tans, S.J., Zon, J.S.: Organoidtracker: Efficient cell tracking using machine learning and manual error correction. PLoS ONE 15(10), 0240802 (2020) https://doi.org/10.1371/journal.pone.0240802
[12]      Lux, F., Matula, P.: Dic image segmentation of dense cell populations by combining deep learning and watershed. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 236–239 (2019). https://doi.org/10.1109/ISBI.2019.8759594
[13]      Ren, W., Wang, X., Tian, J., Tang, Y., Chan, A.B.: Tracking-by-counting: Using network flows on crowd density maps for tracking multiple targets. IEEE Transactions on Image Processing 30, 1439–1452 (2021)
[14]      Wang, Z., Yin, L., Wang, Z.: A new approach for cell detection and tracking. IEEE Access 7, 99889–99899 (2019) https://doi.org/10.1109/ACCESS.2019.2930532
[15]      Scherr, T., L¨offler, K., B¨ohland, M., Mikut, R.: Cell segmentation and tracking using cnn-based distance predictions and a graph-based matching strategy. PLoS ONE 15(12), 0243219 (2020) https://doi.org/10.1371/journal.pone.0243219
[16]      Hayashida, J., Bise, R.: Cell tracking with deep learning for cell detection and motion estimation in low-frame-rate. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 11764, pp. 397–405 (2019). https://doi.org/10.1007/978-3-030-32239-744
[17]      Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M.W., Valen, D.V.: Deep learning for cellular image analysis. Nature Methods 16, 1233–1246(2019) https://doi.org/10.1038/s41592-019-0403-1
[18]      Cheng, H.-J., Hsu, C.-H., Hung, C.-L., Lin, C.-Y.: A review for cell and particle tracking on microscopy images using algorithms and deep learning technologies. Biomedical Journal 45, 465–471 (2021)
[19]      Fran¸cani, A.O.: Analysis of the performance of U-Net neural networks for the segmentation of living cells (2022)
[20]      R. Yazdi and H. Khotanlou, “A Survey on Automated Cell Tracking: Challenges and Solutions,” To apear in Multimedia Tools and Applications, 2024.
[21]      Nishimura, K., Bise, R.: Spatial-temporal mitosis detection in phase-contrast microscopy via likelihood map estimation by 3dcnn. In: 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1811–1815 (2020). https://doi.org/10.1109/EMBC44109.2020.9179175
[22]      Li, Y., Rose, F., Pietro, F., Morin, X., Genovesio, A.: Detection and tracking of overlapping cell nuclei for large scale mitosis analyses. BMC Bioinformatics 17, 183 (2016) https://doi.org/10.1186/s12859-016-1030-9
[23]      Gilad, T., Reyes, J., Chen, J.-Y., Lahav, G., Riklin-Raviv, T.: Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy. Bioinformatics 35(15), 2644–2653 (2019) https://doi.org/10.1093/bioinformatics/bty1034
[24]      Lu, Y., Liu, A., Chen, M., Nie, W., Su, Y.: Sequential saliency guided deep neural network for joint mitosis identification and localization in time-lapse phase contrast microscopy images. IEEE Journal of Biomedical and Health Informatics 24, 1367–1378 (2020)
[25]      Mao, Y., Han, L., Yin, Z.: Cell mitosis event analysis in phase contrast microscopy images using deep learning. Medical image analysis 57, 32–43 (2019)
[26]      Su, Y., Lu, Y., Liu, J., Chen, M., Liu, A.: Spatio-temporal mitosis detection in time-lapse phase-contrast microscopy image sequences: A benchmark. IEEE Transactions on Medical Imaging 40, 1319–1328 (2021)
[27]      Ma, M., Shi, Y., Li, W., Gao, Y., Xu, J.: A novel two-stage deep method for mitosis detection in breast cancer histology images. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3892–3897 (2018). https://doi.org/10.1109/ICPR.2018.8546192
[28]      Su, Y., Lu, Y., Chen, M., Liu, A.: Spatiotemporal joint mitosis detection using cnn-lstm network in time-lapse phase contrast microscopy images. IEEE Access 5, 18033–18041 (2017)
[29]      Zhou, Y., Mao, H., Yi, Z.: Cell mitosis detection using deep neural networks. Knowl. Based Syst. 137, 19–28 (2017)
[30]      Zhao, M., Jha, A., LIU, Q., Millis, B.A., Mahadevan-Jansen, A., Lu, L., Landman, B.A., Tyska, M.J., Huo, Y.: Faster mean-shift: Gpu-accelerated clustering for cosine embedding-based cell segmentation and tracking. Medical image analysis 71, 102048 (2021) https://doi.org/10.1016/j.media.2021.102048
[31]      Jo, H., Han, J., Kim, Y.S., Lee, Y., Yang, S.: A novel method for effective cell segmentation and tracking in phase contrast microscopic images. Sensors 21(10), 3516 (2021) https://doi.org/10.3390/s21103516
[32]      Liang, P., Chen, J., Zhang, Y., Wang, H., Zheng, H., Gu, P., Chen, D.: Intracker: An integrated detector-tracker framework for cell detection and tracking. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 332–337 (2020). https://doi.org/10.1109/CBMS49503.2020.00069
[33]      Wang, Z., Yin, L., Wang, Z.: A new approach for cell detection and tracking. IEEE Access 7, 99889–99899 (2019) https://doi.org/10.1109/ACCESS.2019.2930532
[34]      Shailja, S., Jiang, J., Manjunath, B.S.: Semi supervised segmentation and graph-based tracking of 3d nuclei in time-lapse microscopy. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp.385–389 (2021). https://doi.org/10.1109/ISBI48211.2021.9433943
[35]      Jun, B., Ahmadzadegan, A., Ardekani, A., Solorio, L., Vlachos, P.: Multi-feature-based robust cell tracking. Annals of Biomedical Engineering 51,604–617 (2023) https://doi.org/10.1007/s10439-022-03073-1
[36]      T¨uretken, E., Wang, X., Becker, C.J., Haubold, C., Fua, P.V.: Network flow integer programming to track elliptical cells in time-lapse sequences. IEEE Transactions on Medical Imaging 36, 942–951 (2017)
[37]      Dewan, M.A.A., Ahmad, M.O., Swamy, M.N.S.: Tracking biological cells in time-lapse microscopy: An adaptive technique combining motion and topological features. IEEE Transactions on Biomedical Engineering 58, 1637–1647 (2011)
[38]      Zhou, Z., Wang, F., Xi, W., Chen, H., Gao, P., He, C.: Joint multiframe detection and segmentation for multi-cell tracking. In: Image and Graphics, ICIG 2019, pp. 435–446 (2019). https://doi.org/10.1007/978-3-030-34110-7 36
[39]      Xiao, P., Zhong, L.: Tracking of non-dividing cells by using generalized voronoi diagram. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2684–2687 (2017).
[40]      Boukari, F., Makrogiannis, S.: Automated cell tracking using motion prediction-based matching and event handling. IEEE/ACM Transactions on Computational Biology and Bioinformatics 17(3), 959–971 (2020) https://doi.org/10.1109/TCBB.2018.2875684
[41]      Arbelle, A., Drayman, N., Bray, M.-A., Alon, U., Carpenter, A.E., Riklin-Raviv, T.: Analysis of high-throughput microscopy videos: Catching up with cell dynamics. In: MICCAI, vol. 9351 (2015). https://doi.org/10.1007/978-3-319-24574-4 26
[42]      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) https://doi.org/10.1016/j.imavis.2016.11.010
[43]      Nguyen, T.T.D., Shim, C., Kim, W.: Biological cell tracking and lineage inference via random finite sets. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 339–343 (2021).
[44]      Magnusson, K.E.G., Jalden, J., Gilbert, P.M., Blau, H.M.: Global linking of cell tracks using the viterbi algorithm. IEEE Transactions on Medical Imaging 34(4), 911–929 (2015) https://doi.org/10.1109/TMI.2014.2370951
[45]      Li, R., Gao, Q., Rohr, K.: Multi-object dynamic memory network for cell tracking in time-lapse microscopy images. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1029–1032 (2021).
[46]      Chen, Y., Song, Y., Zhang, C., Zhang, F., O’Donnell, L., Chrzanowski, W., Cai, W.: Celltrack r-cnn: A novel end-to-end deep neural network for cell segmentation and tracking in microscopy images. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 779–782 (2021).
[47]      Zhu, Y., Meijering, E.H.W.: Automatic improvement of deep learning-based cell segmentation in time-lapse microscopy by neural architecture search. Bioinformatics 37, 4844–4850 (2021)
[48]      D. Svoboda and V. Ulman, “Mitogen: A framework for generating 3d synthetic time-lapse sequences of cell populations in fluorescence microscopy,” IEEE Transactions on Medical Imaging, vol. 36, pp. 310–321, 2017.
[49]      S. Pizer, R. Johnston, J. Ericksen, B. Yankaskas, and K. Muller, “Contrast-limited adaptive histogram equalization: speed and effectiveness,” in [1990] Proceedings of the First Conference on Visualization in Biomedical Computing, 1990, pp. 337–345.
[50]      R. Yazdi and H. Khotanlou, “MaxSigNet: Light learnable layer for semantic cell segmentation”. Biomed. Signal Process. Control., 95, 106464. https://doi.org/10.1016/j.bspc.2024.106464
[51]      R. Yazdi, H. Khotanlou, E. Alighardash, and M. Zolfaghari, “Edge detection method based on the differences in intensities of rotating kernel borders,” in 2023 6th International Conference on Pattern Recognition and Image Analysis (IPRIA), 2023, pp. 1–5.
[52]      Yazdi, Reza and Khotanlou, Hassan, Cell Division Detection Using Cassini Oval Theory. Available at SSRN: https://ssrn.com/abstract=4752301 or http://dx.doi.org/10.2139/ssrn.4752301
[53]      Lewis, J.P. (2001). Fast Normalized Cross-Correlation. Ind. Light Magic. 10.
[54]      Loshchilov, I., & Hutter, F. (2017). Decoupled Weight Decay Regularization. International Conference on Learning Representations.
[55]      Ben-Haim, T., & Riklin-Raviv, T. (2022). Graph Neural Network for Cell Tracking in Microscopy Videos. ECCV 2022: 17th European Conference, Tel Aviv, Israel, 2022, Proceedings, Pages610–626
[56]      Schacherer, D., Ritter, C., & Rohr, K. (2021). Multiple Hypothesis Tracking with Integrated Cell Division Detection. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 165-168.
[57]      Panteli, A., Gupta, D.K., Bruijn, N.D., & Gavves, E. (2020). Siamese Tracking of Cell Behaviour Patterns. PMLR 121:570-587.