Computer Vision-Aided 2D Error Assessment and Correction for Helix Bioprinting

¹ State Key Laboratory of Metal Matrix Composites, School of Material Science and Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai, 200240, China

² Department of Orthopaedics, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi Key Laboratory of Basic and Translational Research of Bone and Joint Degenerative Diseases, Guangxi Biomedical Materials Engineering Research Center for Bone and Joint Degenerative Diseases, Baise, 533000, Guangxi, China

³ 3D Printing Clinical Translational and Regenerative Medicine Center, Shenzhen Shekou People’s Hospital

⁴ Department of Stomatology, Shenzhen Shekou People’s Hospital

IJB 2022, 8(2), 547 https://doi.org/10.18063/ijb.v8i2.547

Submitted: 30 December 2021 | Accepted: 7 February 2022 | Published: 7 February 2022

© 2022 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

Download PDF

Cite

XML

Abstract

Bioprinting is an emerging multidisciplinary technology for organ manufacturing, tissue repair, and drug screening. The manufacture of organs in a layer-by-layer manner is a characteristic of bioprinting technology, which can also determine the accuracy of constructs confined by the printing resolution. The lack of sufficient resolution will result in defect generation during the printing process and the inability to complete the manufacture of complex organs. A computer vision-based method is proposed in this study to detect the deviation of the printed helix from the reference trajectory and calculate the modified reference trajectory through error vector compensation. The new printing helix trajectory resulting from the modified reference trajectory error is significantly reduced compared with the original helix trajectory and the correction efficiency exceeded 90%.

Keywords

Bioprinting

Computer vision

Error detection

Quality assurance

Sobel operator

References

1. Lawlor KT, Vanslambrouck JM, Higgins JW, et al., 2021, Cellular Extrusion Bioprinting Improves Kidney Organoid Reproducibility and Conformation. Nat Mater, 20:260–71. https://doi.org/10.1038/s41563-020-00853-9

2. Cui X, Breitenkamp K, Finn MG, et al., 2012, Direct Human Cartilage Repair Using Three-Dimensional Bioprinting Technology. Tissue Eng A, 18:1304–12. https://doi.org/10.1089/ten.tea.2011.0543

3. Yanez M, Rincon J, Dones A, et al., 2015, In Vivo Assessment of Printed Microvasculature in a Bilayer Skin Graft to Treat Full-Thickness Wounds. Tissue Eng A, 21:224–33. https://doi.org/10.1089/ten.tea.2013.0561

4. Lee A, Hudson AR, Shiwarski DJ, et al., 2019, 3D Bioprinting of Collagen to Rebuild Components of the Human Heart. Science, 365:482–7. https://doi.org/10.1126/science.aav9051

5. Daly AC, Prendergast ME, Hughes AJ, et al., 2021, Bioprinting for the Biologist. Cell, 184:18–32. https://doi.org/10.1016/j.cell.2020.12.002

6. Jose RR, Rodriguez MJ, Dixon TA, et al., 2016, Evolution of Bioinks and Additive Manufacturing Technologies for 3D Bioprinting. ACS Biomater Sci Eng, 2:1662–78. https://doi.org/10.1021/acsbiomaterials.6b00088

7. Murphy SV, Atala A, 2014, 3D Bioprinting of Tissues and Organs. Nat Biotechnol, 32:773–85. https://doi.org/10.1038/nbt.2958

8. Zorlutuna P, Jeong JH, Kong H, et al., 2011, Stereolithography-Based Hydrogel Microenvironments to Examine Cellular Interactions. Adv Funct Mater, 21:3642–51. https://doi.org/10.1002/adfm.201101023

9. Darwish LR, El-Wakad MT, Farag MM, 2021, Towards an Ultra-Affordable Three-Dimensional Bioprinter: A Heated Inductive-Enabled Syringe Pump Extrusion Multifunction Module for Open-Source Fused Deposition Modeling Three-Dimensional Printers. J Manuf Sci Eng, 143:125001. https://doi.org/10.1115/1.4050824

10. Duan B, Hockaday LA, Kang KH, et al., 2008, 3D Bioprinting of Heterogeneous Aortic Valve Conduits with Alginate/ Gelatin Hydrogels. Bone, 23:1–7. https://doi.org/10.1002/jbm.a.34420.3D

11. Hinton TJ, Lee A, Feinberg AW, 2017, 3D Bioprinting from the Micrometer to Millimeter Length Scales: Size Does Matter. Curr Opin Biomed Eng, 1:31–7. https://doi.org/10.1016/j.cobme.2017.02.004

12. Ozbolat IT, Hospodiuk M, 2016, Current Advances and Future Perspectives in Extrusion-Based Bioprinting. Biomaterials, 76:321–43. https://doi.org/10.1016/j.biomaterials.2015.10.076

13. McBeth C, Lauer J, Ottersbach M, et al., 2017, 3D Bioprinting of GelMA Scaffolds Triggers Mineral Deposition by Primary Human Osteoblasts. Biofabrication, 9:015009. https://doi.org/10.1088/1758-5090/aa53bd

14. Dababneh AB, Ozbolat IT, 2014, Bioprinting Technology: A Current State-of-the-Art Review. J Manuf Sci Eng Trans ASME, 136:1–11. https://doi.org/10.1115/1.4028512

15. Armstrong AA, Norato J, Alleyne AG, et al., 2020, Direct Process Feedback in Extrusion-Based 3D Bioprinting. Biofabrication, 12:015017. https://doi.org/10.1088/1758-5090/ab4d97

16. Armstrong AA, Alleyne AG, Johnson AJ, 2020, 1D and 2D Error Assessment and Correction for Extrusion-Based Bioprinting Using Process Sensing and Control Strategies. Biofabrication, 12:045023. https://doi.org/10.1088/1758-5090/aba8ee

17. Hockaday LA, Kang KH, Colangelo NW, et al., 2012, Rapid 3D Printing of Anatomically Accurate and Mechanically Heterogeneous Aortic Valve Hydrogel Scaffolds. Biofabrication, 4:035005. https://doi.org/10.1088/1758-5082/4/3/035005

18. Rastogi P, Kandasubramanian B, 2019, Review of Alginate-Based Hydrogel Bioprinting for Application in Tissue Engineering. Biofabrication, 11:042001. https://doi.org/10.1088/1758-5090/ab331e

19. Fisch P, Broguiere N, Finkielsztein S, et al., 2021, Bioprinting of Cartilaginous Auricular Constructs Utilizing an Enzymatically Crosslinkable Bioink. Adv Funct Mater, 31:1–15. https://doi.org/10.1002/adfm.202008261

20. Wibisono A, Mursanto P, 2020, Multi Region-Based Feature Connected Layer (RB-FCL) of Deep Learning Models for Bone Age Assessment. J Big Data, 7:67. https://doi.org/10.1186/s40537-020-00347-0

21. Qiu C, Ravi GA, Attallah MM, 2015, Microstructural Control During Direct Laser Deposition of a β-Titanium Alloy. Materials and Design, 81:21–30. https://doi.org/10.1016/j.matdes.2015.05.031

22. Fu G, Corradi P, Menciassi A, Dario P, 2011, An Integrated Triangulation Laser Scanner for Obstacle Detection of Miniature Mobile Robots in Indoor Environment. IEEE/ASME Trans Mechatron, 16:778–83. https://doi:10.1109/TMECH.2010.2084582

23. Yang JS, Xie YJ, He W, 2011, Research Progress on Chemical Modification of Alginate: A Review. Carbohydr Polym, 84:33–9. https://doi.org/10.1016/j.carbpol.2010.11.048

24. Wang B, Wan Y, Zheng Y, et al., 2019, Alginate-Based Composites for Environmental Applications: A Critical Review. Crit Rev Environ Sci Technol, 49:318–56. https://doi.org/10.1080/10643389.2018.1547621

25. Thakur S, Sharma B, Verma A, et al., 2018, Recent Progress in Sodium Alginate Based Sustainable Hydrogels for Environmental Applications. J Clean Prod, 198:143–59. https://doi.org/10.1016/j.jclepro.2018.06.259

26. Axpe E, Oyen ML, 2016, Applications of Alginate-Based Bioinks in 3D Bioprinting. Int J Mol Sci, 17:1976. https://doi.org/10.3390/ijms17121976

27. Abasalizadeh F, Moghaddam SV, Alizadeh E, et al., 2020, Erratum: Alginate-Based Hydrogels as Drug Delivery Vehicles in Cancer Treatment and Their Applications in Wound Dressing and 3D Bioprinting. J Biol Eng, 14:8 https://doi.org/10.1186/s13036-020-00239-0

28. Farjah A, Owlia P, Siadat SD, et al., 2015, Immunological Evaluation of an Alginate-Based Conjugate as a Vaccine Candidate Against Pseudomonas Aeruginosa. Apmis, 123:175–83. https://doi.org/10.1111/apm.12337

29. Rastogi P, Kandasubramanian B, 2019, Review of Alginate-Based Hydrogel Bioprinting for Application in Tissue Engineering. Biofabrication, 11:042001. https://doi.org/10.1088/1758-5090/ab331e

30. Reakasame S, Boccaccini AR, 2018, Oxidized Alginate-Based Hydrogels for Tissue Engineering Applications: A Review. Biomacromolecules, 19:3–21. https://doi.org/10.1021/acs.biomac.7b01331

31. Bandy HT, Donmez MA, Gilsinn DE, et al., 2001, A Methodology for Compensating Errors Detected by Process-Intermittent Inspection. Maryland, United States: National Institute of Standards and Technology.

32. Kanopoulos N, Vasanthavada N, Baker RL, 1988, Design of an Image Edge Detection Filter Using the Sobel Operator. IEEE J Solid-State Circuits, 23:358–67. https://doi.org/10.1109/4.996

33. Gao W, Yang L, Zhang X, et al., 2010, An Improved Sobel Edge Detection. In: Proceedings-2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT No. 5, p. 67–71. https://doi.org/10.1109/ICCSIT.2010.5563693

34. Vairalkar MM, 2012, Edge Detection of Images Using Sobel Operator. Int J Emerg Technol Adv Eng, 2:291–3.

35. Perra C, Massidda F, Giusto DD, 2005, Image Blockiness Evaluation Based on Sobel Operator. In: Proceedings-International Conference on Image Processing, ICIP No. 1, p. 389–92. https://doi.org/10.1109/ICIP.2005.1529769

36. Celebi ME, Celiker F, Kingravi HA, 2011, On Euclidean Norm Approximations. Pattern Recognit, 44:278–83. https://doi.org/10.1016/j.patcog.2010.08.028

Previous article in this issue

Next article in this issue

International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing