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International Joint Conference on Biomedical Engineering Systems and Technologies

BIOSTEC 2020: Biomedical Engineering Systems and Technologies pp 93–115Cite as

Deep Learning for the Automated Feature Labelling of 3-Dimensional Imaged Placenta

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1400))

Abstract

3-D visualisation of cellular structures within the placenta is important for advancing research into the factors determining fetal growth, which are linked to chronic disease risk and quality of lifelong health. 2-D analysis can be challenging, and spatial interaction between structures can be easily missed, but obtaining 3-D structural images is extremely labour-intensive due to the high level of rigorous manual processing required. Deep neural networks are used to automate this previously manual process to construct fast and accurate 3-D structural images, which can be used for 3-D image analysis. The deep networks described in this chapter are trained to label both single cell, a fibroblast and a pericyte, and multicellular, endothelial, structures from within serial block-face scanning electron microscopy placental imaging. Automated labels are equivalent, pixel-to-pixel, to manual labels by over 98% on average over all cell structures and network architectures, and are able to successfully label unseen regions and stacks.

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References

  1. Mackay, B., et al.: Automated 3-D labelling of fibroblasts and endothelial cells in SEM-imaged placenta using deep learning. In: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOIMAGING 2020, INSTICC, vol. 2, pp. 46–53. SciTePress, Malta (2020)

    Google Scholar 

  2. Palaiologou, E., et al.: Human placental villi contain stromal macrovesicles associated with networks of stellate cells. J Anat. 236(1), 132–141 (2019)

    Article  Google Scholar 

  3. Lewis, R.M., Cleal, J.K., Hanson, M.A.: Review: placenta, evolution and lifelong health. Placenta 33, S28–S32 (2012)

    Article  Google Scholar 

  4. Lewis, R.M., Pearson-Farr, J.E.: Multiscale three-dimensional imaging of the placenta. Placenta. https://doi.org/10.1016/j.placenta.2020.01.016. (Article in press)

  5. Burton, G.J.: Scanning electron microscopy of intervillous connections in the mature human placenta. J. Anat. 147, 245–254 (1986)

    Google Scholar 

  6. Mayhew, T.M.: Morphomics: an integral part of systems biology of the human placenta. Placenta 36(4), 329–340 (2015)

    Article  Google Scholar 

  7. Wang, Y., Zhao, S.: Vascular Biology of the Placenta. Morgan & Claypool, San Rafael (2010)

    Book  Google Scholar 

  8. Cahill, L.S., et al.: Feto- and utero-placental vascular adaptions to chronic maternal hypoxia in the mouse. J. Physiol 596(15), 3285–3297 (2018)

    Article  Google Scholar 

  9. Kherlopian, A.R., et al.: A review of imaging techniques for systems biology. BMC Syst Biol. 2, 74 (2008)

    Article  Google Scholar 

  10. Denk, W., Horstmann, H.: Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2(100), e329 (2004)

    Article  Google Scholar 

  11. Kazemian, A., Hooshmandabbasi, R., Schraner, E.M., Boos, A., Klisch, K.: Evolutionary implications of fetal and maternal microvillous surfaces in epitheliochorial placentae. J. Morphol. 280(4), 615–622 (2019)

    Article  Google Scholar 

  12. Palaiologou, E., et al.: Serial block-face scanning electron microscopy reveals novel intercellular connections in human term placental microvasculature. J. Anat. 237, 1–9 (2020)

    Article  Google Scholar 

  13. Deerinck, T.J., Bushong, E.A., Lev-Ram, V., Shu, X., Tsien, R.Y., Ellisman, M.H.: Enhancing serial block-face scanning electron microscopy to enable high resolution 3-D nanohistology of cells and tissues. Microsc. Microanal. 16(2), 1138–1139 (2010)

    Article  Google Scholar 

  14. Zachow, S., Zilske, M., Hege, H.: 3-D reconstruction of individual anatomy from medical image data: segmentation and geometry processing. Konrad-Zuse-Zentrum für Informationstechnik Berlin, Berlin (2007)

    Google Scholar 

  15. Pugin, E., Zhiznyakov, A.: Histogram method of image binarization based on fuzzy pixel representation. In: Dynamics of Systems, Mechanisms and Machines 2017 (Dynamics), p. 17467698. IEEE, Omsk (2017)

    Google Scholar 

  16. Yoo, K.D., et al.: A machine learning approach using survival statistics to predict graft survival in kidney transplant recipients: a multicenter cohort study. Sci. Rep. 7, 8904 (2017)

    Article  Google Scholar 

  17. Jamshidi, A., Pelletier, J., Martel-Pelletier, J.: Machine-learning-based patient-specific prediction models for knee osteoarthritis. Nat. Rev. Rheumatol. 15, 49–60 (2019)

    Article  Google Scholar 

  18. Google Cloud: Vision AI| Derive Image Insights via ML. Google. https://cloud.google.com/vision/. Accessed 14 Nov 2019

  19. Grant-Jacob, J.A., et al.: Real-time particle pollution sensing using machine learning. Opt. Express 26(21), 27237–27246 (2018)

    Article  Google Scholar 

  20. Krizhevsky, A., Sutskever, L., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems 25, pp. 1090–1098 (2012)

    Google Scholar 

  21. Szegedy, C., et al.: Intriguing properties of neural networks. arXiv:1312.6199v4 (2014)

  22. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  23. Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556v6 (2015)

  24. Kermany, D.S., Goldbaum, M., Cai, W., Lewis, M.A., Xia, H., Zhang, K.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)

    Article  Google Scholar 

  27. Haenssle, H.A., et al.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29, 1836–1842 (2018)

    Article  Google Scholar 

  28. Le Cun, Y., Huang, F. J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. II-97 (2004)

    Google Scholar 

  29. Lo, S.B., Chan, H., Freedman, M.T., Min, S.K.: Artificial convolution neural network for medical image pattern recognition. Neural Netw. 8(7–8), 1201–1214 (1995)

    Article  Google Scholar 

  30. Le Cun, Y., Bengio, Y.: Convolutional Networks for Images Speech and Time-Series. The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995)

    Google Scholar 

  31. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., Le Cun, Y.: OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. arXiv:1312.6229v4 (2013)

  32. Donahue, J., et al.: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2625–2634 (2015)

    Google Scholar 

  33. Zhang, X., Le Cun, Y.: Text Understanding from Scratch. arXiv:1502.01710v5 (2015)

  34. Dong, A., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)

    Article  Google Scholar 

  35. Simard, D., Steinkraus, P.Y., Platt, J.C.: Best practices for convolutional neural networks. In: Proceedings of the Document Analysis and Recognition, pp. 958–963 (2003)

    Google Scholar 

  36. Vaillant, R., Monrocq, C., Le Cun, Y.: Original approach for the localisation of objects in images. Proc. Vis. Image Sig. Process. 141, 245–250 (1994)

    Article  Google Scholar 

  37. Nowlan, S., Platt, J.: A convolutional neural network hand tracker. In: Advances in Neural Information Processing Systems, pp. 901–908 (1995)

    Google Scholar 

  38. Le Cun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems (1990)23

    Google Scholar 

  39. Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex. J. Physiol. 106, 160 (1962)

    Google Scholar 

  40. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Google Scholar 

  41. Fukushima, K., Miyake, S., Ito, T.: Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Trans. Syst. Man Cybern. 13(5), 826–834 (1983)

    Article  Google Scholar 

  42. Felleman, D.J., Essen, D.C.V.: Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991)

    Article  Google Scholar 

  43. Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets. arXiv:1411.1784v1 (2014)

  44. Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A. A.: Context Encoders: Feature Learning by Inpainting. arXiv:1604.07379v2 (2016)

  45. Isola, P., Zhue, J., Zhou T., Efros, A.A.: Image-to-Image Translation with Conditional Adversarial Networks. arXiv:1611.07004v3 (2018)

  46. Esteva, A., et al.: A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019)

    Article  Google Scholar 

  47. 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

    Chapter  Google Scholar 

  48. Long, J., Shelhamer, E., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation. arXiv:1411.4038 (2014)

  49. Falk, T., et al.: U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67–70 (2019)

    Article  Google Scholar 

  50. Barreto, R.S.N., Romagnolli, P., Cereta, A.D., Coimbra-Campos, L.M.C., Birbrair, A., Miglino, M.A.: Pericytes in the placenta: role in placental development and homeostasis. In: Birbrair, A. (ed.) Pericyte Biology in Different Organs. AEMB, vol. 1122, pp. 125–151. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11093-2_8

    Chapter  Google Scholar 

  51. Bergers, G., Song, S.: The role of pericytes in blood-vessel formation and maintenance. Neuro Oncol. 7(4), 452–464 (2005)

    Article  Google Scholar 

  52. Grant-Jacob, J.A., et al.: A neural lens for super-resolution biological imaging. J. Phys. Commun. 3(6), 065004 (2019)

    Article  Google Scholar 

  53. Santosh, K.C., Roy, P.P.: Arrow detection in biomedical images using sequential classifier. Int. J. Mach. Learn. Cybern. 9(6), 993–1006 (2016). https://doi.org/10.1007/s13042-016-0623-y

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Optoelectronics Research Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK

    Benita S. Mackay, James A. Grant-Jacob, Robert W. Eason & Ben Mills

  2. Institute of Developmental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6HW, UK

    Rohan Lewis

Authors
  1. Benita S. Mackay
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  2. James A. Grant-Jacob
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  3. Robert W. Eason
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  4. Rohan Lewis
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  5. Ben Mills
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Corresponding author

Correspondence to Benita S. Mackay .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xuesong Ye

  2. Fraunhofer Portugal Research Center for Assistive Information and Communication Solutions, Porto, Portugal

    Filipe Soares

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. Universidad Politécnica de Madrid, Madrid, Spain

    Pedro Gómez Vilda

  5. University of Milano-Bicocca, Milan, Italy

    Federico Cabitza

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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Mackay, B.S., Grant-Jacob, J.A., Eason, R.W., Lewis, R., Mills, B. (2021). Deep Learning for the Automated Feature Labelling of 3-Dimensional Imaged Placenta. In: Ye, X., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2020. Communications in Computer and Information Science, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-72379-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-72379-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72378-1

  • Online ISBN: 978-3-030-72379-8

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