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Neuron Characterization in Complex Cultures Using a Combined YOLO and U-Net Segmentation Approach

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Abstract

This work presents a novel approach for the automated characterization of neurons in primary culture from phase-contrast images. Direct characterization of neurons from these images is very challenging due to the complexity of the images. Over time in culture neurons change shape and size, and extend neuronal connections (i.e., neurites) between them. Also, cultured neurons are accompanied by other cell types, mainly glial cells, which may be difficult to distinguish from neurons. In this study we have applied U-Net segmentation to isolate and extract individual neurons, while also addressing the challenges posed by the presence of other cell types and structures in the culture. We then used YOLO object detection to classify and localize neurons accurately. Combining these two models, we have been able to successfully characterize neurons within these complex cultures. Our findings demonstrate the potential of this approach for a more comprehensive analysis of neurons in challenging environments. The present work is part of a larger study aimed to fully analyze neuronal behaviour throughout development.

This research has been funded by the Instituto Universitario de Tecnología Industrial de Asturias (IUTA), grant SV-22-GIJÓN-1- 22, Agencia Estatal de Investigación, grant MCI-21-PID2020-119087RB-I00, and GRUPIN SV-PA-21-AYUD/2021/52132.

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References

  1. Arbelle, A., Raviv, T.R.: Microscopy cell segmentation via adversarial neural networks

    Google Scholar 

  2. Arbelle, A., Raviv, T.R.: Microscopy cell segmentation via convolutional LSTM networks

    Google Scholar 

  3. Bamford, P., Lovell, B.C.: Unsupervised cell nucleus segmentation with active contours. Signal Process. 71, 203–213

    Google Scholar 

  4. Beucher, S.: Use of watersheds in contour detection

    Google Scholar 

  5. Cabrera-Garcia, D., Warm, D., de la Fuente, P., Fernández-Sánchez, M.T., Novelli, A., Villanueva-Balsera, J.M.: Early prediction of developing spontaneous activity in cultured neuronal networks. Scient. Reports 11(1) (2021)

    Google Scholar 

  6. Cetin, S., Knez, D., Gobec, S., Kos, J., Pišlar, A.: Cell models for Alzheimer’s and parkinson’s disease: at the interface of biology and drug discovery. Biomed. Pharmacotherapy 149, 112–924 (2022)

    Google Scholar 

  7. Eglen, R., Gilchrist, A., Reisine, T.: An overview of drug screening using primary and embryonic stem cells. Combinatorial Chem. High Throughput Screen. 11(7), 566–572 (2008)

    Article  Google Scholar 

  8. Fang, Y., Guo, X., Chen, K., Zhou, Z., Ye, Q.: Accurate and automated detection of surface knots on sawn timbers using yolo-v5 model. BioResources 16(3), 5390–5406 (2021)

    Article  Google Scholar 

  9. Fernández, M.T., Zitko, V., Gascón, S., Novelli, A.: The marine toxin okadaic acid is a potent neurotoxin for cultured cerebellar neurons. Life Sci. 49(19), PL157–PL162 (1991)

    Google Scholar 

  10. Gupta, A., et al.: Deep learning in image cytometry: A review. Cytometry A 95, 366–380 (2019)

    Article  Google Scholar 

  11. He, F., Huang, X., Wang, X., Qiu, S., Jiang, F., Ling, S.H.: A neuron image segmentation method based deep boltzmann machine and cv model. Comput. Med. Imaging Graph. 89, 101–871 (2021)

    Google Scholar 

  12. Ho, S.Y., Chao, C.Y., Huang, H.L., Chiu, T.W., Charoenkwan, P., Hwang, E.: NeurphologyJ: an automatic neuronal morphology quantification method and its application in pharmacological discovery. BMC Bioinformatics 12(1) (2011)

    Google Scholar 

  13. Hung, J., et al.: Applying faster R-CNN for object detection on malaria images. arXiv:1804.09548 (2018)

  14. Işin, A., Direkoǧlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods, pp. 317–324. Elsevier B.V. (2016)

    Google Scholar 

  15. Jiang, J., Kao, P.Y., Belteton, S.A., Szymanski, D.B., Manjunath, B.S.: Accurate 3d cell segmentation using deep feature and CRF refinement (2019)

    Google Scholar 

  16. Karri, M., Annavarapu, C.S.R., Mallik, S., Zhao, Z., Acharya, U.R.: Multi-class nucleus detection and classification using deep convolutional neural network with enhanced high dimensional dissimilarity translation model on cervical cells. Biocybern. Biomed. Eng. 42, 797–814 (2022)

    Article  Google Scholar 

  17. Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, O., Kummervold, P.E.: Short communication: detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4) (2021)

    Google Scholar 

  18. Lee, S.Y., et al.: Image analysis using machine learning for automated detection of hemoglobin h inclusions in blood smears: A method for morphologic detection of rare cells. J. Pathol. Inform. 12(1), 18 (2021)

    Article  MathSciNet  Google Scholar 

  19. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  20. Liu, R., Dai, W., Wu, T., Wang, M., Wan, S., Liu, J.: Aimic: deep learning for microscopic image classification. Comput. Methods Programs Biomed. 226, 107–162 (2022)

    Google Scholar 

  21. Lou, X., Schiegg, M., Hamprecht, F.A.: Active structured learning for cell tracking: algorithm, framework, and usability. IEEE Trans. Med. Imaging 33, 849–860 (2014)

    Article  Google Scholar 

  22. Meijering, E.: Cell segmentation: 50 years down the road [life sciences]. IEEE Signal Process. Mag. 29, 140–145 (2012)

    Article  Google Scholar 

  23. Pérez-Gómez, A., Novelli, A., Fernández-Sánchez, M.T.: Na\(<\)sup\(>+<\)/sup\(>\)/k\(<\)sup\(>+<\)/sup\(>\)-ATPase inhibitor palytoxin enhances vulnerability of cultured cerebellar neurons to domoic acid via sodium-dependent mechanisms. J. Neurochem. (2010)

    Google Scholar 

  24. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems 28

    Google Scholar 

  25. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation

    Google Scholar 

  26. Rosca, A., et al.: Impact of environmental neurotoxic: current methods and usefulness of human stem cells. Heliyon 6(12), e05,773 (2020)

    Google Scholar 

  27. Schmidt, U., Weigert, M., Broaddus, C., Myers, G.: Cell detection with star-convex polygons (2018)

    Google Scholar 

  28. Wang, Z., Jin, L., Wang, S., Xu, H.: Apple stem/calyx real-time recognition using yolo-v5 algorithm for fruit automatic loading system. Postharvest Biology and Technology 185 (2022)

    Google Scholar 

  29. Wood, L.B., et al.: Identification of neurotoxic cytokines by profiling Alzheimer’s disease tissues and neuron culture viability screening. Sci. Reports 5(1) (2015)

    Google Scholar 

  30. Wu, H., Souedet, N., Jan, C., Clouchoux, C., Delzescaux, T.: A general deep learning framework for neuron instance segmentation based on efficient unet and morphological post-processing. Comput. Biol. Med. 150, 106–180 (2022)

    Google Scholar 

  31. Wu, Q., Merchant, F.A., Castleman, K.R.: Microscope image processing, 1st edn. Elsevier/Academic Press

    Google Scholar 

  32. Yi, J., et al.: Multi-scale cell instance segmentation with keypoint graph based bounding boxes

    Google Scholar 

  33. Yin, C., et al.: Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data. Sci. Reports 10(1), 15,078

    Google Scholar 

  34. Zhang, L., Lu, L., Nogues, I., Summers, R.M., Liu, S., Yao, J.: Deeppap: deep convolutional networks for cervical cell classification (2018)

    Google Scholar 

  35. Zhu, N., Liu, C., Singer, Z.S., Danino, T., Laine, A.F., Guo, J.: Segmentation with residual attention u-net and an edge-enhancement approach preserves cell shape features

    Google Scholar 

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Correspondence to Ángel Río-Álvarez .

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Puerta, P. et al. (2023). Neuron Characterization in Complex Cultures Using a Combined YOLO and U-Net Segmentation Approach. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_9

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