Error assessment and correction for extrusion- based bioprinting using computer vision method

¹ 1 State Key Laboratory of Metal Matrix Composites, School of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

² National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai 200240, China

³ 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, Baise, 533000, Guangxi, China

⁴ 3D Printing Clinical Translational and Regenerative Medicine Center, Shenzhen Shekou People’s Hospital, Shenzhen, 518060, China

⁵ Department of Stomatology, Shenzhen Shekou People’s Hospital, Shenzhen, 518060, China

IJB 2023, 9(1), 644 https://doi.org/10.18063/ijb.v9i1.644

Received: 14 July 2022 | Accepted: 30 August 2022 | Published online: 16 November 2022

(This article belongs to the Special Issue Related to 3D Printing Technology and Materials)

© 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 offers a new approach to addressing the organ shortage crisis. Despite recent technological advances, insufficient printing resolution continues to be one of the reasons that impede the development of bioprinting. Normally, machine axes movement cannot be reliably used to predict material placement, and the printing path tends to deviate from the predetermined designed reference trajectory in varying degrees. Therefore, a computer vision-based method was proposed in this study to correct trajectory deviation and improve printing accuracy. The image algorithm calculated the deviation between the printed trajectory and the reference trajectory to generate an error vector. Furthermore, the axes trajectory was modified according to the normal vector approach in the second printing to compensate for the deviation error. The highest correction efficiency that could be achieved was 91%. More significantly, we discovered that the correction results, for the first time, were in a normal distribution instead of a random distribution.

Keywords

Bioprinting

Computer vision

Error detection

Sobel operator

References

1. Hart A, Smith JM, Skeans MA, et al., 2020, OPTN/SRTR 2018 annual data report: Kidney. Am J Transplant, 20(s1): 20–130. https://doi.org/10.1111/ajt.15672

2. Abouna GM, 2008, Organ shortage crisis: Problems and possible solutions. Transplant Proc, 40(1): 34–38. https://doi.org/10.1016/j.transproceed.2007.11.067

3. Croome KP, Lee DD, Keaveny AP, et al., 2017, Noneligible donors as a strategy to decrease the organ shortage. Am J Transplant, 17(6): 1649–1655. https://doi.org/10.1111/ajt.14163

4. Bastani B, 2020, The present and future of transplant organ shortage: Some potential remedies. J Nephrol, 33(2): 277–288. https://doi.org/10.1007/s40620-019-00634-x

5. Vijayavenkataraman S, Yan WC, Lu WF, et al., 2018, 3D bioprinting of tissues and organs for regenerative medicine. Adv Drug Deliv Rev, 132: 296–332. https://doi.org/10.1016/j.addr.2018.07.004

6. Pedde RD, Mirani B, Navaei A, et al., 2017, Emerging biofabrication strategies for engineering complex tissue constructs. Adv Mater, 29(19): 1–27. https://doi.org/10.1002/adma.201606061

7. van Pel DM, Harada K, Song D, et al., 2018, Modelling glioma invasion using 3D bioprinting and scaffold-free 3D culture. J Cell Commun Signal, 12(4): 723–730. https://doi.org/10.1007/s12079-018-0469-z

8. Liu C, Yang C, Liu J, et al., 2022, Medical high-entropy alloy: Outstanding mechanical properties and superb biological compatibility. Frontiers in Bioengineering and Biotechnology, 1436. https://doi.org/10.3389/fbioe.2022.952536

9. Yu F, Choudhury D, 2019, Microfluidic bioprinting for organon-a-chip models. Drug Discov Today, 24(6): 1248–1257. https://doi.org/10.1016/j.drudis.2019.03.025

10. Li S, Tian X, Fan J, et al., 2019, Chitosans for tissue repair and organ three-dimensional (3D) bioprinting. Micromachines, 10(11): 765. https://doi.org/10.3390/mi10110765

11. Tseng H, Gage JA, Shen T, et al., 2015, A spheroid toxicity assay using magnetic 3D bioprinting and real-time mobile device-based imaging. Sci Rep, 5: 1–11. https://doi.org/10.1038/srep13987 Peng W, Datta P, Ayan B, et al., 2017, 3D bioprinting for drug discovery and development in pharmaceutics. Acta Biomater, 57: 26–46. https://doi.org/10.1016/j.actbio.2017.05.025

13. Daly AC, Prendergast ME, Hughes AJ, et al., 2021, Bioprinting for the biologist. Cell, 184(1): 18–32. https://doi.org/10.1016/j.cell.2020.12.002

14. Rastogi P, Kandasubramanian B, 2019, Review of alginate based hydrogel bioprinting for application in tissue engineering. Biofabrication, 11(4): 042001. https://doi.org/10.1088/1758-5090/ab331e

15. Jose RR, Rodriguez MJ, Dixon TA, et al., 2016, Evolution of bioinks and additive manufacturing technologies for 3D bioprinting. ACS Biomater Sci Eng, 2(10): 1662–1678. https://doi.org/10.1021/acsbiomaterials.6b00088

16. Ozbolat IT, Hospodiuk M, 2016, Current advances and future perspectives in extrusion-based bioprinting. Biomaterials, 76: 321–343. https://doi.org/10.1016/j.biomaterials.2015.10.076

17. Paxton N, Smolan W, Böck T, et al., 2017, Proposal to assess printability of bioinks for extrusion-based bioprinting and evaluation of rheological properties governing bioprintability. Biofabrication, 9(4): 044107. https://doi.org/10.1088/1758-5090/aa8dd8

18. Chung JHY, Naficy S, Yue Z, et al., 2013, Bio-ink properties and printability for extrusion printing living cells. Biomater Sci, 1(7): 763–773. https://doi.org/10.1039/c3bm00012e

19. Blaeser A, Duarte Campos DF, Puster U, et al., Controlling shear stress in 3D bioprinting is a key factor to balance printing resolution and stem cell integrity. Adv Healthc Mater, 5(3): 326–333. https://doi.org/10.1002/adhm.201500677

20. Lee JM, Ng WL, Yeong WY, 2019, Resolution and shape in bioprinting: Strategizing towards complex tissue and organ printing. Appl Phys Rev, 6(1): 011307. https://doi.org/10.1063/1.5053909

21. Suntornnond R, Tan EYS, An J, et al., 2016, A mathematical model on the resolution of extrusion bioprinting for the development of new bioinks. Materials (Basel), 9(9): 756. https://doi.org/10.3390/ma9090756

22. Wang Z, Abdulla R, Parker B, et al., 2015, A simple and high resolution stereolithography-based 3D bioprinting system using visible light crosslinkable bioinks. Biofabrication, 7(4): 45009. https://doi.org/10.1088/1758-5090/7/4/045009

23. Lee A, Hudson AR, Shiwarski DJ, et al., 2019, 3D bioprinting of collagen to rebuild components of the human heart. Science (80-. ), 365(6452): 482–487. https://doi.org/10.1126/science.aav9051

24. Bertassoni LE, Cardoso JC, Manoharan V, et al., 2014, Direct-write bioprinting of cell-laden methacrylated gelatin hydrogels. Biofabrication, 6(2): 024105. https://doi.org/10.1088/1758-5082/6/2/024105

25. Armstrong AA, Alleyne AG, Wagoner Johnson AJ, 2020, 1D and 2D error assessment and correction for extrusion-based bioprinting using process sensing and control strategies. Biofabrication, 12(4): 045023. https://doi.org/10.1088/1758-5090/aba8ee

26. Armstrong AA, Pfeil A, Alleyne AG, et al., 2021, Process monitoring and control strategies in extrusion-based bioprinting to fabricate spatially graded structures. Bioprinting, 21(September 2020): e00126. https://doi.org/10.1016/j.bprint.2020.e00126

27. Liu C, Liu J, Yang C, et al., 2022, Computer vision-aided 2D error assessment and correction for helix bioprinting. Int J Bioprinting, 8(2): 174–186. https://doi.org/10.18063/ijb.v8i2.547

28. Axpe E, Oyen ML, 2016, Applications of alginate-based bioinks in 3D bioprinting. Int J Mol Sci, 17(12): 1976. https://doi.org/10.3390/ijms17121976

29. Wang B, Wan Y, Zheng Y, et al., 2019, Alginate-based composites for environmental applications: A critical review. Crit Rev Environ Sci Technol, 49(4): 318–356. https://doi.org/10.1080/10643389.2018.1547621

30. Rastogi P, Kandasubramanian B, 2019, Review of alginate based hydrogel bioprinting for application in tissue engineering. Biofabrication, 11(4): 042001. https://doi.org/10.1088/1758-5090/ab331e

31. Jain D, Bar-Shalom D, 2014, Alginate drug delivery systems: Application in context of pharmaceutical and biomedical research. Drug Dev Ind Pharm, 40(12): 1576–1584. https://doi.org/10.3109/03639045.2014.917657

32. AbdelAllah NH, Gaber Y, Rashed ME, et al., 2020, Alginate-coated chitosan nanoparticles act as effective adjuvant for hepatitis A vaccine in mice. Int J Biol Macromol, 152: 904–912. https://doi.org/10.1016/j.ijbiomac.2020.02.287

33. Ghosh M, Halperin-Sternfeld M, Grinberg I, et al., 2019, Injectable alginate-peptide composite hydrogel as a scaffold for bone tissue regeneration. Nanomaterials, 9(4): 497. https://doi.org/10.3390/nano9040497

34. Łabowska MB, Cierluk K, Jankowska AM, et al., 2021, A review on the adaption of alginate-gelatin hydrogels for 3D cultures and bioprinting. Materials (Basel), 14(4): 1–28. https://doi.org/10.3390/ma14040858

35. Vairalkar MK, 2012, Edge detection of images using Sobel operator. Int J Emerg Technol Adv Eng, 2(1): 291–293 [Online]. Available: www.ijetae.com

36. Wang W, 2009, Reach on Sobel operator for vehicle recognition. 2009 International Joint Conference on Artificial Intelligence, 448–451. https://doi.org/10.1109/JCAI.2009.54

37. Kanopoulos N, Vasanthavada N, Baker RL, 1988, Design of an image edge detection filter using the Sobel operator. IEEE J Solid-State Circuits, 23(2): 358–367. https://doi.org/10.1109/4.996

38. Armstrong AA, Norato J, Alleyne AG, et al., 2020, Direct process feedback in extrusion-based 3D bioprinting. Biofabrication, 12(1): 015017. https://doi.org/10.1088/1758-5090/ab4d97

39. Liu C, Wang L, Lu W, et al., 2022, Computer vision-aided bioprinting for bone research. Bone Res, 10(1): 1–14. https://doi.org/10.1038/s41413-022-00192-2

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