journal article·2024-09

Deep Learning Methods for Microstructural Image Analysis: The State-of-the-Art and Future Perspectives

Khaled Alrfou, Tian Zhao, Amir Kordijazi

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Industrial Vision Systems and Defect Detection · Machine Learning in Materials Science · Image Processing Techniques and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (100+)

Show source-provided references
1. McCauley JW (2016) An introduction to materials by design including a dynamic stress environment. Eng Ceram Curr Stat Future Prospect 1–2810.1002/9781119100430.ch1 ↗
2. Ueno T, Rhone TD, Hou Z, Mizoguchi T, Tsuda K (2016) Combo: an efficient Bayesian optimization library for materials science. Mater Discov 4:18–2110.1016/j.md.2016.04.001 ↗
3. Alberi K, Nardelli MB, Zakutayev A, Mitas L, Curtarolo S, Jain A, Fornari M, Marzari N, Takeuchi I, Green ML et al (2018) The 2019 materials by design roadmap. J Phys D Appl Phys 52(1):01300110.1088/1361-6463/aad926 ↗
4. Kordijazi A, Zhao T, Zhang J, Alrfou K, Rohatgi P (2021) A review of application of machine learning in design, synthesis, and characterization of metal matrix composites: current status and emerging applications. JOM 73(7):2060–207410.1007/s11837-021-04701-2 ↗
5. Kalidindi SR (2013) Microstructure informatics. In: Informatics for materials science and engineering. Elsevier, 443–46610.1016/B978-0-12-394399-6.00018-7 ↗
6. Liu H, Yucel B, Wheeler D, Ganapathysubramanian B, Kalidindi SR, Wodo O (2022) How important is microstructural feature selection for data-driven structure-property mapping? MRS Commun 12(1):95–10310.1557/s43579-021-00147-4 ↗
7. Holm EA, Cohn R, Gao N, Kitahara AR, Matson TP, Lei B, Yarasi SR (2020) Overview: computer vision and machine learning for microstructural characterization and analysis. Metall Mater Trans A 51:5985–599910.1007/s11661-020-06008-4 ↗
8. Yang Z, Li X, Catherine Brinson L, Choudhary AN, Chen W, Agrawal A (2018) Microstructural materials design via deep adversarial learning methodology. J Mech Des 140(11):11141610.1115/1.4041371 ↗
9. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–232410.1109/5.726791 ↗
10. Heo B, Yun S, Han D, Chun S, Choe J, Oh SJ (2021) Rethinking spatial dimensions of vision transformers. In: Proceedings of the IEEE/CVF international conference on computer vision, 11936–1194510.1109/ICCV48922.2021.01172 ↗
11. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30
12. Cecen A, Dai H, Yabansu YC, Kalidindi SR, Song L (2018) Material structure-property linkages using three-dimensional convolutional neural networks. Acta Mater 146:76–8410.1016/j.actamat.2017.11.053 ↗
13. Yang Z, Yabansu YC, Al-Bahrani R, Liao W-K, Choudhary AN, Kalidindi SR, Agrawal A (2018) Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets. Comput Mater Sci 151:278–28710.1016/j.commatsci.2018.05.014 ↗
14. Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Van Esesn BC, Awwal AAS, Asari VK (2018) The history began from alexnet: a comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164
15. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–11710.1016/j.neunet.2014.09.003 ↗
16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25
17. Kelleher JD, Mac Namee B, D’Arcy A (2020) Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press
18. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 580–58710.1109/CVPR.2014.81 ↗
19. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks in Computer Vision-ECCV, 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part I 13. 818–83310.1007/978-3-319-10590-1_53 ↗
20. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 770–77810.1109/CVPR.2016.90 ↗
21. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
22. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 1–910.1109/CVPR.2015.7298594 ↗
23. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 3431–344010.1109/CVPR.2015.7298965 ↗
24. Veit A, Wilber MJ, Belongie S (2016) Residual networks behave like ensembles of relatively shallow networks. Adv Neural Inf Process Syst 29
25. Abdi M, Nahavandi S (2016) Multi-residual networks: improving the speed and accuracy of residual networks. arXiv preprint arXiv:1609.05672
26. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI, 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. Springer 2015:234–24110.1007/978-3-319-24574-4_28 ↗
27. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–249510.1109/TPAMI.2016.2644615 ↗
28. Rühle B, Krumrey JF, Hodoroaba V-D (2021) Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks. Sci Rep 11(1):494210.1038/s41598-021-84287-6 ↗
29. Iqbal T, Ali H (2018) Generative adversarial network for medical images (MI-GAN). J Med Syst 42:1–1110.1007/s10916-018-1072-9 ↗
30. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International conference on machine learning. PMLR, 214–223
31. Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 4681–469010.1109/CVPR.2017.19 ↗
32. Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, 2223–223210.1109/ICCV.2017.244 ↗
33. Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784
34. Higgins I, Pal A, Rusu A, Matthey L, Burgess C, Pritzel A, Botvinick M, Blundell C, Lerchner A (2017) Darla: improving zero-shot transfer in reinforcement learning. In: International Conference on Machine Learning. PMLR, 1480–1490
35. Kingma DP, Welling M (2013) Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114
36. Guo M-H, Xu T-X, Liu J-J, Liu Z-N, Jiang P-T, Mu T-J, Zhang S-H, Martin RR, Cheng M-M, Hu S-M (2022) Attention mechanisms in computer vision: a survey. Comput visual Media 8(3):331–36810.1007/s41095-022-0271-y ↗
37. Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2022) Transformers in vision: a survey. ACM Comput Surv (CSUR) 54(10s):1–4110.1145/3505244 ↗
38. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929
39. Ye L, Rochan M, Liu Z, Wang Y (2019) Cross-modal self-attention network for referring image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 10502–1051110.1109/CVPR.2019.01075 ↗
40. Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable detr: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159
41. Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision. Springer, 213–22910.1007/978-3-030-58452-8_13 ↗
42. Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: International conference on machine learning. PMLR, 7354–7363
43. Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision, 10012–1002210.1109/ICCV48922.2021.00986 ↗
44. Han K, Xiao A, Wu E, Guo J, Xu C, Wang Y (2021) Transformer in transformer. Adv Neural Inf Process Syst 34:15908–15 919
45. Chu X, Tian Z, Wang Y, Zhang B, Ren H, Wei X, Xia H, Shen C (2021) Twins: revisiting the design of spatial attention in vision transformers. Adv Neural Inf Process Syst 34:9355–9366
46. Huang Z, Ben Y, Luo G, Cheng P, Yu G, Fu B (2021) Shuffle transformer: rethinking spatial shuffle for vision transformer. arXiv preprint arXiv:2106.03650
47. Larmuseau M, Sluydts M, Theuwissen K, Duprez L, Dhaene T, Cottenier S (2020) Compact representations of microstructure images using triplet networks. npj Comput Mater 6(1):15610.1038/s41524-020-00423-2 ↗
48. Campbell A, Murray P, Yakushina E, Marshall S, Ion W (2018) New methods for automatic quantification of microstructural features using digital image processing. Mater Design 141:395–40610.1016/j.matdes.2017.12.049 ↗
49. Duval L, Moreaud M, Couprie C, Jeulin D, Talbot H, Angulo J (2014) Image processing for materials characterization: issues, challenges and opportunities. In: IEEE international conference on image processing (icip). IEEE 2014:4862–486610.1109/ICIP.2014.7025985 ↗
50. Lindemann GR, Chao P, Nikitin V, De Andrade V, De Graef M, Shahani AJ (2024) Complexity and evolution of a three-phase eutectic during coarsening uncovered by 4d nano-imaging. Acta Mater 266, 119684. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S135964542400037510.1016/j.actamat.2024.119684 ↗
51. Marturi N, Dembélé S, Piat N (2014) Scanning electron microscope image signal-to-noise ratio monitoring for micro-nanomanipulation. Scanning J Scanning Microsc 36(4):419–429
52. Yang T, Luo Y, Ji W, Yang G (2021) Advancing biological super-resolution microscopy through deep learning: a brief review. Biophys Rep 7(4):25310.52601/bpr.2021.210019 ↗
53. Chen X, Chen J, Han X, Zhao C, Zhang D, Zhu K, Su Y (2020) A light-weighted CNN model for wafer structural defect detection. IEEE Access 8:24 006-24 01810.1109/ACCESS.2020.2970461 ↗
54. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 4510–452010.1109/CVPR.2018.00474 ↗
55. Gupta R, Anand V, Gupta S, Koundal D (2023) Deep learning model for defect analysis in industry using casting images. Expert Syst Appl 232:12075810.1016/j.eswa.2023.120758 ↗
56. Mulewicz B, Korpala G, Kusiak J, Prahl U (2019) Autonomous interpretation of the microstructure of steels and special alloys. In Materials science forum, vol. 949. Trans Tech Publ, 24–3110.4028/www.scientific.net/MSF.949.24 ↗
57. Elbana R, Mostafa R, Elkeran A (2020) Data processing for automatic classification of spheroidite microstructure using deep learning based on fcnns. Int J Mech Mechatron Eng 20(03):18–31
58. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861
59. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 1251–125810.1109/CVPR.2017.195 ↗
60. Banerjee D, Sparks TD (2022) Comparing transfer learning to feature optimization in microstructure classification. Iscience, 25(2)10.1016/j.isci.2022.103774 ↗
61. Christen P, Hand DJ, Kirielle N (2023) A review of the F-measure: its history, properties, criticism, and alternatives. ACM Comput Surv 56(3). [Online]. https://doi.org/10.1145/360636710.1145/3606367 ↗
62. Muñoz-Rodenas J, García-Sevilla F, Coello-Sobrino J, Martínez-Martínez A, Miguel-Eguía V (2023) Effectiveness of machine-learning and deep-learning strategies for the classification of heat treatments applied to low-carbon steels based on microstructural analysis. Appl Sci 13(6):347910.3390/app13063479 ↗
63. Baskaran A, Kane G, Biggs K, Hull R, Lewis D (2020) Adaptive characterization of microstructure dataset using a two stage machine learning approach. Comput Mater Sci 177:10959310.1016/j.commatsci.2020.109593 ↗
64. Luo Q, Holm EA, Wang C (2021) A transfer learning approach for improved classification of carbon nanomaterials from tem images. Nanoscale Adv 3(1):206–21310.1039/D0NA00634C ↗
65. Ivo R F, Rodrigues D d A, Bezerra G M, Freitas F N, de Abreu H F G, Rebouças Filho P P (2020) Non-grain oriented electrical steel photomicrograph classification using transfer learning. J Market Res 9(4):8580–8591
66. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 4700–470810.1109/CVPR.2017.243 ↗
67. Xiang S, Chen X, Fan Z, Chen T, Lian X (2022) A deep learning-aided prediction approach for creep rupture time of Fe–Cr–Ni heat-resistant alloys by integrating textual and visual features. J Market Res 18:268–281
68. Cheloee Darabi A, Rastgordani S, Khoshbin M, Guski V, Schmauder S (2023) Hybrid data-driven deep learning framework for material mechanical properties prediction with the focus on dual-phase steel microstructures. Materials 16(1):44710.3390/ma16010447 ↗
69. Sadre R, Ophus C, Butko A, Weber GH (2021) Deep learning segmentation of complex features in atomic-resolution phase-contrast transmission electron microscopy images. Microsc Microanal 27(4):804–81410.1017/S1431927621000167 ↗
70. Alrfou K, Kordijazi A, Rohatgi P, Zhao T (2022) Synergy of unsupervised and supervised machine learning methods for the segmentation of the graphite particles in the microstructure of ductile iron. Mater Today Commun 30:10317410.1016/j.mtcomm.2022.103174 ↗
71. Shah A, Schiller JA, Ramos I, Serrano J, Adams DK, Tawfick S, Ertekin E (2023) Automated image segmentation of scanning electron microscopy images of graphene using u-net neural network. Mater Today Commun 35:10612710.1016/j.mtcomm.2023.106127 ↗
72. Ajioka F, Wang Z-L, Ogawa T, Adachi Y (2020) Development of high accuracy segmentation model for microstructure of steel by deep learning. ISIJ Int 60(5):954–95910.2355/isijinternational.ISIJINT-2019-568 ↗
73. Azimi SM, Britz D, Engstler M, Fritz M, Mücklich F (2018) Advanced steel microstructural classification by deep learning methods. Sci Rep 8(1):212810.1038/s41598-018-20037-5 ↗
74. DeCost BL, Lei B, Francis T, Holm EA (2019) High throughput quantitative metallography for complex microstructures using deep learning: a case study in ultrahigh carbon steel. Microsc Microanal 25(1):21–2910.1017/S1431927618015635 ↗
75. Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022) Swin-unet: unet-like pure transformer for medical image segmentation. In: European conference on computer vision. Springer, 205–21810.1007/978-3-031-25066-8_9 ↗
76. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. Springer 2018:3–1110.1007/978-3-030-00889-5_1 ↗
77. Fotos G, Campbell A, Murray P, Yakushina E (2023) Deep learning enhanced watershed for microstructural analysis using a boundary class semantic segmentation. J Mater Sci 58(36):14 390-14 41010.1007/s10853-023-08901-w ↗
78. Ma DW, Wu XJ, Yang H (2019) Efficient small object detection with an improved region proposal networks. In: IOP Conference Series: Materials Science and Engineering, vol. 533, no. 1. IOP Publishing, 01206210.1088/1757-899X/533/1/012062 ↗
79. Girshick R (2015) Fast r-cnn. In: IEEE international conference on computer vision (ICCV) 2015:1440–144810.1109/ICCV.2015.169 ↗
80. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Cortes C, Lawrence N, Lee D, Sugiyama M, and Garnett R (eds) Advances in neural information processing systems, vol. 28. Curran Associates, Inc
81. He K, Gkioxari G, Dollár P, Girshick RB (2017) Mask r-cnn. In: ICCV. IEEE Computer Society, 2980–298810.1109/ICCV.2017.322 ↗
82. Fu L, Yu H, Shah M, Simmons J, Wang S (2020) Crystallographic symmetry for data augmentation in detecting dendrite cores. Electron Imaging 32:1–710.1117/1.JEI.29.2.023020 ↗
83. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28
84. Fehlemann N, Suarez Aguilera AL, Sandfeld S, Bexter F, Neite M, Lenz D, Könemann M, Münstermann S (2023) Identification of martensite bands in dual-phase steels: a deep learning object detection approach using faster region-based-convolutional neural network. Steel Res Int 94:220083610.1002/srin.202200836 ↗
85. Masubuchi S, Watanabe E, Seo Y, Okazaki S, Sasagawa T, Watanabe K, Taniguchi T, Machida T (2020) Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials. npj 2d Mater Appl 4:310.1038/s41699-020-0137-z ↗
86. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, 2961–296910.1109/ICCV.2017.322 ↗
87. Agbozo R, Jin W (2020) Quantitative metallographic analysis of gcr15 microstructure using mask r-cnn. J Korean Soc Precis Eng 37(5):361–36910.7736/JKSPE.019.144 ↗
88. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: Computer Vision-ECCV, 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part V 13. 740–75510.1007/978-3-319-10602-1_48 ↗
89. Li M, Chen D, Liu S, Liu F (2021) Prior mask r-cnn based on graph cuts loss and size input for precipitation measurement. IEEE Trans Instrum Meas 70:1–15
90. Wang Z, Fan L, Lu Y, Mao J, Huang L, Zhou J (2022) Tesn: transformers enhanced segmentation network for accurate nanoparticle size measurement of TEM images. Powder Technol 407:11767310.1016/j.powtec.2022.117673 ↗
91. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, and Weinberger K (eds) Advances in neural information processing systems vol. 27. Curran Associates, Inc
92. Kingma DP, Welling M (2019) An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), 307–392, . [Online]. Available: http://dx.doi.org/10.1561/220000005610.1561/2200000056 ↗
93. Chen L, Zhang W, Nie Z, Li S, Pan F (2021) Generative models for inverse design of inorganic solid materials. J Mater Inform 1(4)10.20517/jmi.2021.07 ↗
94. Long T, Fortunato NM, Opahle I, Zhang Y, Samathrakis I, Shen C, Gutfleisch O, Zhang H (2021) Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures. npj Comput Mater 7(1):6610.1038/s41524-021-00526-4 ↗
95. Chun S, Roy S, Nguyen YT, Choi JB, Udaykumar H, Baek SS (2020) Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials. Sci Rep 10(1):1330710.1038/s41598-020-70149-0 ↗
96. Kim SW, Kang S-H, Kim S-J, Lee S (2021) Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning. Sci Rep 11(1):590210.1038/s41598-021-85407-y ↗
97. Ibtehaz N, Rahman MS (2020) Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw 121:74–8710.1016/j.neunet.2019.08.025 ↗
98. Cao Z, Liu Q, Liu Q, Yu X, Kruzic JJ, Li X (2023) A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti–6Al–4V. npj Comput Mater 9(1):19510.1038/s41524-023-01152-y ↗
99. Ding G, Liu Y, Zhang R, Xin HL (2019) A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond. Sci Rep 9(1):1280310.1038/s41598-019-49267-x ↗
100. Yang X, De Andrade V, Scullin W, Dyer EL, Kasthuri N, De Carlo F, Gürsoy D (2018) Low-dose X-ray tomography through a deep convolutional neural network. Sci Rep 8(1):257510.1038/s41598-018-19426-7 ↗