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Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM)

  • Microstructure Characterization: Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
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Abstract

Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along with appropriate training encoder–decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.

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References

  1. Q. Zhang, S. Niverty, A.S.S. Singaravelu, J.J. Williams, E. Guo, T. Jing, and N. Chawla, Mater. Charact. 148, 52. (2019).

    Article  Google Scholar 

  2. J.M. Yu, N. Wanderka, A. Rack, R. Daudin, E. Boller, H. Markötter, A. Manzoni, F. Vogel, T. Arlt, I. Manke, and J. Banhart, Acta Mater. 129, 194. (2017).

    Article  Google Scholar 

  3. Q. Krol, and H. Löwe, Acta Mater. 151, 478. (2018).

    Article  Google Scholar 

  4. N. Limodin, L. Salvo, E. Boller, M. Suéry, M. Felberbaum, S. Gailliègue, and K. Madi, Acta Mater. 57, 2300. (2009).

    Article  Google Scholar 

  5. C.S. Kaira, V. De Andrade, S. Singh, C. Kantzos, A. Kirubanandham, F. De Carlo, and N. Chawla, Adv. Mater. 29, 1703482. (2017).

    Article  Google Scholar 

  6. E. Boulard, C. Denoual, A. Dewaele, A. King, Y. Le Godec, and N. Guignot, Acta Mater. 192, 30. (2020).

    Article  Google Scholar 

  7. S. Niverty, C. Kale, K.N. Solanki, and N. Chawla, Corros. Sci. 185, 109429. (2021).

    Article  Google Scholar 

  8. M.B. Kelly, S. Niverty, and N. Chawla, J. Alloys Compds. 818, 152918. (2020).

    Article  Google Scholar 

  9. A.S.S. Singaravelu, J.J. Williams, H.D. Goyal, S. Niverty, S.S. Singh, T.J. Stannard, X. Xiao, and N. Chawla, Metall. Mater. Trans. A 51, 28. (2020).

    Article  Google Scholar 

  10. M.B. Kelly, S. Niverty, and N. Chawla, Acta Mater. 189, 118. (2020).

    Article  Google Scholar 

  11. V. Mazars, O. Caty, G. Couégnat, A. Bouterf, S. Roux, S. Denneulin, J. Pailhès, and G.L. Vignoles, Acta Mater. 140, 130. (2017).

    Article  Google Scholar 

  12. S. Niverty, (2020).

  13. S.S. Singh, T.J. Stannard, X. Xiao, and N. Chawla, JOM 69, 1404. (2017).

    Article  Google Scholar 

  14. J. Samei, C. Pelligra, M. Amirmaleki, and D.S. Wilkinson, Mater. Lett. 269, 127664. (2020).

    Article  Google Scholar 

  15. A. Isaac, F. Sket, W. Reimers, B. Camin, G. Sauthoff, and A.R. Pyzalla, Mater. Sci. Eng. A 478, 108. (2008).

    Article  Google Scholar 

  16. T. Lacondemine, J. Réthoré, É. Maire, F. Célarié, P. Houizot, C. Roux-Langlois, C.M. Schlepütz, and T. Rouxel, Acta Mater. 179, 424. (2019).

    Article  Google Scholar 

  17. H.A. Bale, A. Haboub, A.A. MacDowell, J.R. Nasiatka, D.Y. Parkinson, B.N. Cox, D.B. Marshall, and R.O. Ritchie, Nature Mater. 12, 40. (2013).

    Article  Google Scholar 

  18. A.S.S. Singaravelu, J.J. Williams, J. Ruppert, M. Henderson, C. Holmes, and N. Chawla, J. Mater. Sci. (2020).

  19. B.M. Patterson, L. Kuettner, T. Shear, K. Henderson, M.J. Herman, A. Ionita, N. Chawla, J. Williams, T. Sun, K. Fezzaa, X. Xiao, and C. Welch, J. Mater. Sci. 55, 11353. (2020).

    Article  Google Scholar 

  20. C.S. Kaira, C.R. Mayer, V. De Andrade, F. De Carlo, and N. Chawla, Microsc. Microanal. 22, 808. (2016).

    Article  Google Scholar 

  21. X. Yang, D. Gürsoy, C. Phatak, V. De Andrade, E.B. Gulsoy, and F. De Carlo, Microsc. Microanal. 22, 240. (2016).

    Article  Google Scholar 

  22. C.S. Kaira, V. De Andrade, S.S. Singh, C. Kantzos, F. De Carlo, and N. Chawla, Microsc. Microanal. 23, 2220. (2017).

    Article  Google Scholar 

  23. C. Shashank Kaira, X. Yang, V. De Andrade, F. De Carlo, W. Scullin, D. Gursoy, and N. Chawla, Mater. Charact. 142, 203. (2018).

    Article  Google Scholar 

  24. Y. Wang, J. Gao, Y. Ren, V. De Andrade, and A.J. Shahani, JOM 72, 2965. (2020).

    Article  Google Scholar 

  25. L.J. Ausderau, H.J. Gonzalez Malabet, J.R. Buckley, V. De Andrade, Y. Liu, and G.J. Nelson, JOM 69, 1478. (2017).

    Article  Google Scholar 

  26. V. De Andrade, A. Deriy, M.J. Wojcik, D. Gürsoy, D. Shu, K. Fezzaa and F. De Carlo, SPIE Newsroom, (2016).

  27. D. Gürsoy, F. De Carlo, X. Xiao, and C. Jacobsen, J. Synchrotron Radiat. 21, 1188. (2014).

    Article  Google Scholar 

  28. J.J. Williams, Z. Flom, A.A. Amell, N. Chawla, X. Xiao, and F. De Carlo, Acta Mater. 58, 6194. (2010).

    Article  Google Scholar 

  29. J.C.E. Mertens, J.J. Williams, and N. Chawla, Nucl. Instrum. Methods Phys. Res. A 800, 82. (2015).

    Article  Google Scholar 

  30. C. Gobert, A. Kudzal, J. Sietins, C. Mock, J. Sun, and B. McWilliams, Add. Manuf. 36, 101460. (2020).

    Google Scholar 

  31. A. Kumar, and G.K.H. Pang, IEEE Trans. Syst. Man Cybernet. B 32, 553. (2002).

    Article  Google Scholar 

  32. P.I. Guntoro, G. Tiu, Y. Ghorbani, C. Lund, and J. Rosenkranz, Miner. Eng. 142, 105882. (2019).

    Article  Google Scholar 

  33. C. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2006).

    MATH  Google Scholar 

  34. T.F. Gonzalez, Handbook of Approximation Algorithms and Metaheuristics 1 (Taylor & Francis, London, 2007).

    Book  MATH  Google Scholar 

  35. B. Ma, X. Ban, H. Huang, Y. Chen, W. Liu, and Y. Zhi, Symmetry 10, 107. (2018).

    Article  Google Scholar 

  36. T. Stan, Z.T. Thompson, and P.W. Voorhees, Mater. Charact. 160, 110119. (2020).

    Article  Google Scholar 

  37. A. Tekawade, B.A. Sforzo, K.E. Matusik, A.L. Kastengren and C.F. Powell, in Developments in X-Ray Tomography XII, ed. B. Müller and G. Wang (SPIE, 2019), p. 67.

  38. S. Evsevleev, S. Paciornik, and G. Bruno, Adv. Eng. Mater. 22, 1. (2020).

    Article  Google Scholar 

  39. D. Chen, D. Guo, S. Liu, and F. Liu, Symmetry 12, 639. (2020).

    Article  Google Scholar 

  40. X. Yang, F. De Carlo, C. Phatak, and D. Gürsoy, J. Synchrotron Radiat. 24, 469. (2017).

    Article  Google Scholar 

  41. X. Yang, V. De Andrade, W. Scullin, E.L. Dyer, N. Kasthuri, F. De Carlo, and D. Gürsoy, Sci. Rep. 8, 2575. (2018).

    Article  Google Scholar 

  42. I. Rizwan, I. Haque, and J. Neubert, Inform. Med. Unlock. 18, 100297. (2020).

    Article  Google Scholar 

  43. Z. Zhou, R. Siddiquee, N. Tajbakhsh, and J. Liang, 1 (n.d.).

  44. W. Zhang, X. He, W. Li, Z. Zhang, Y. Luo, L. Su, and P. Wang, Image Vis. Comput. 93, 103824. (2020).

    Article  Google Scholar 

  45. X. Liu, Z. Deng, and Y. Yang, Artif. Intell. Rev. 52, 1089. (2019).

    Article  Google Scholar 

  46. Q. Liu, A.B. Salberg and R. Jenssen, International Geoscience and Remote Sensing Symposium (IGARSS) 2018-July, 6943 (2018).

  47. I. Goodfellow, Y. Benjio, and A. Courville, Deep Learning (MIT, Cambridge, 2016).

    MATH  Google Scholar 

  48. Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, in (2018), pp. 3–11.

  49. L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff and H. Adam, Proceedings of the European Conference on Computer Vision (ECCV) 801 (2018).

  50. L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, IEEE Trans. Pattern Anal. Mach. Intell. 40, 834. (2018).

    Article  Google Scholar 

  51. H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia, Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 2017-Jan, 6230 (2017).

  52. S.S. Singh, C. Schwartzstein, J.J. Williams, X. Xiao, F. De Carlo, and N. Chawla, J. Alloys Compds. 602, 163. (2014).

    Article  Google Scholar 

  53. M. Gao, C.R. Feng, and R.P. Wei, Metall. Mater. Trans. A 29, 1145. (1998).

    Article  Google Scholar 

  54. S. Dey, M.K. Gunjan, and I. Chattoraj, Corros. Sci. 50, 2895. (2008).

    Article  Google Scholar 

  55. W. Tian, S. Li, B. Wang, J. Liu, and M. Yu, Corros. Sci. 113, 1. (2016).

    Article  Google Scholar 

  56. O. Ronneberger, P. Fischer, and T. Brox, in (2015), pp. 234–241.

  57. G. Shi, S. Guan and X. Yang, (2011).

  58. K. Jamart, Z. Xiong, G. D. Maso Talou, M. K. Stiles and J. Zhao, Front. Cardiovasc. Med., 7, (2020).

  59. C.S. Kaira, C. Kantzos, J.J. Williams, V. De Andrade, F. De Carlo, and N. Chawla, Acta Mater. 144, 419. (2018).

    Article  Google Scholar 

  60. J. Bertels, T. Eelbode, M. Berman, D. Vandermeulen, F. Maes, R. Bisschops,and M. B. Blaschko, in (2019), pp. 92–100.

  61. S. Jadon, 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020 (2020).

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Acknowledgements

H.T., S.N., and N.C. are grateful for financial support from the Office of Naval Research (ONR) under Contract No. N00014-10-1-0350 (Dr. W. Mullins, Program Manager). We acknowledge the use of resources at Beamline 32-ID-C of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.

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Authors and Affiliations

  1. School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Hamidreza Torbati-Sarraf, Sridhar Niverty & Nikhilesh Chawla

  2. School of Arts Media and Engineering, Arizona State University, Tempe, AZ, 85281, USA

    Rajhans Singh, Daniel Barboza & Pavan Turaga

  3. Advanced Photon Source, Argonne National Laboratory, Argonne, IL, 60439, USA

    Vincent De Andrade

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  1. Hamidreza Torbati-Sarraf
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Correspondence to Nikhilesh Chawla.

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Torbati-Sarraf, H., Niverty, S., Singh, R. et al. Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM). JOM 73, 2173–2184 (2021). https://doi.org/10.1007/s11837-021-04706-x

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  • DOI: https://doi.org/10.1007/s11837-021-04706-x

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