A deep learning approach for error detection and quantification in extrusion-based bioprinting
Quality control in extrusion-based bioprinting (EBB) represents a crucial step to: i) reduce the trial-and-error process and associated material consumption, ii) achieve standard results across different set-ups and laboratories to comply with relevant health standards, and iii) so accelerate the tr...
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| Published in: | Materials today : proceedings Vol. 70; pp. 131 - 135 |
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2022
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| Abstract | Quality control in extrusion-based bioprinting (EBB) represents a crucial step to: i) reduce the trial-and-error process and associated material consumption, ii) achieve standard results across different set-ups and laboratories to comply with relevant health standards, and iii) so accelerate the translation of Tissue Engineered products to more impactful clinical applications. In this context, machine learning algorithms represent a key enabling technology that is currently being explored in literature for quality control in EBB, thanks to their ability to learn relevant features from a training dataset and generalize to new, unseen data. In this work, we present a novel application of a deep learning model to EBB, namely a convolutional Long Short-Term Memory (LSTM) autoencoder, to extract a relevant quality measure from videos taken from a frontal view during the printing process. In particular, a comprehensive dataset was built by varying multiple printing parameters and using different EBB set-ups. The data was then used to train the model and validate it using videos containing different types of errors (i.e., under- or over-extrusion). Results highlight that the approach can effectively detect relevant extrusion-related problems in a proportional way to the error magnitude, and so can be applied as a quality control solution for the EBB process. |
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| AbstractList | Quality control in extrusion-based bioprinting (EBB) represents a crucial step to: i) reduce the trial-and-error process and associated material consumption, ii) achieve standard results across different set-ups and laboratories to comply with relevant health standards, and iii) so accelerate the translation of Tissue Engineered products to more impactful clinical applications. In this context, machine learning algorithms represent a key enabling technology that is currently being explored in literature for quality control in EBB, thanks to their ability to learn relevant features from a training dataset and generalize to new, unseen data. In this work, we present a novel application of a deep learning model to EBB, namely a convolutional Long Short-Term Memory (LSTM) autoencoder, to extract a relevant quality measure from videos taken from a frontal view during the printing process. In particular, a comprehensive dataset was built by varying multiple printing parameters and using different EBB set-ups. The data was then used to train the model and validate it using videos containing different types of errors (i.e., under- or over-extrusion). Results highlight that the approach can effectively detect relevant extrusion-related problems in a proportional way to the error magnitude, and so can be applied as a quality control solution for the EBB process. |
| Author | Kai Chua, Chee Vozzi, Giovanni Bonatti, Amedeo Franco De Maria, Carmelo |
| Author_xml | – sequence: 1 givenname: Amedeo Franco surname: Bonatti fullname: Bonatti, Amedeo Franco email: amedeofranco.bonatti@phd.unipi.it organization: Department of Information Engineering and Research Center “Enrico Piaggio”, University of Pisa, Pisa, Italy – sequence: 2 givenname: Giovanni surname: Vozzi fullname: Vozzi, Giovanni email: giovanni.vozzi@unipi.it organization: Department of Information Engineering and Research Center “Enrico Piaggio”, University of Pisa, Pisa, Italy – sequence: 3 givenname: Chee surname: Kai Chua fullname: Kai Chua, Chee email: cheekai_chua@sutd.edu.sg organization: Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore – sequence: 4 givenname: Carmelo surname: De Maria fullname: De Maria, Carmelo email: carmelo.demaria@unipi.it organization: Department of Information Engineering and Research Center “Enrico Piaggio”, University of Pisa, Pisa, Italy |
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| Keywords | Deep learning Convolutional LSTM autoencoder Extrusion-based bioprinting Quality control |
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| References | Chiesa, De Maria, Lapomarda, Fortunato, Montemurro, Di Gesù, Tuan, Vozzi, Gottardi (b0075) 2020; 12 Bonatti, A. F., Fortunato, G. M., Lapomarda A., De Acutis, A., De Maria, C., Vitale-Brovarone, C., Vozzi, G., (2021). Advanced Firmware and Hardware for Multiscale and Multimaterial Bioprinting, Proceedings of the Seventh National Congress of Bioengineering Bonatti, Chiesa, Vozzi, De Maria (b0020) 2021; 24 Di Pietro, Ravizza, Vozzi, Lantada, Ahluwalia, De Maria (b0010) 2019; 3 He, Zhang, Ren, Sun (b0090) 2016 Ruberu, Senadeera, Rana, Gupta, Chung, Yue, Venkatesh, Wallace (b0050) 2021; 22 Ozbolat, Moncal, Gudapati (b0015) 2017; 13 Jin, Zhang, Shao, Gu (b0045) 2021 arXiv preprint arXiv:2003.05991. https://doi.org/10.48550/arXiv.2003.05991. Multimedia systems and Applications V LeCun, Bengio, Hinton (b0025) 2015; 521 Fu, Angeline, Sun (b0040) 2021; 7 Wickramasinghe, Marino, Manic (b0095) 2021; 9 Zhao, Deng, Shen, Liu, Lu, Hua (b0055) 2017 Fan, Wen, Li, Qiu, Levine, Xiao (b0060) 2020; 195 Srivastava, N., Mansimov, E., & Salakhudinov, R. (2015, June). Unsupervised learning of video representations using lstms. In International conference on machine learning (pp. 843-852). PMLR. (Vol. 4861, pp. 325-332). International Society for Optics and Photonics. https://doi.org/10.1117/12.456333. Zhao, M., Bu, J., & Chen, C. (2002, December). Robust background subtraction in HSV color space. In Yu, Jiang (b0030) 2020; 6 Pati, Jang, Lee, Cho (b0005) 2015 Bank, D., Koenigstein, N., & Giryes, R. (2020). Autoencoders. An, Chua, Mironov (b0035) 2021; 7 LeCun (10.1016/j.matpr.2022.09.006_b0025) 2015; 521 Pati (10.1016/j.matpr.2022.09.006_b0005) 2015 Ozbolat (10.1016/j.matpr.2022.09.006_b0015) 2017; 13 10.1016/j.matpr.2022.09.006_b0080 10.1016/j.matpr.2022.09.006_b0070 10.1016/j.matpr.2022.09.006_b0065 Chiesa (10.1016/j.matpr.2022.09.006_b0075) 2020; 12 10.1016/j.matpr.2022.09.006_b0085 An (10.1016/j.matpr.2022.09.006_b0035) 2021; 7 Jin (10.1016/j.matpr.2022.09.006_b0045) 2021 He (10.1016/j.matpr.2022.09.006_b0090) 2016 Wickramasinghe (10.1016/j.matpr.2022.09.006_b0095) 2021; 9 Bonatti (10.1016/j.matpr.2022.09.006_b0020) 2021; 24 Zhao (10.1016/j.matpr.2022.09.006_b0055) 2017 Fu (10.1016/j.matpr.2022.09.006_b0040) 2021; 7 Yu (10.1016/j.matpr.2022.09.006_b0030) 2020; 6 Ruberu (10.1016/j.matpr.2022.09.006_b0050) 2021; 22 Di Pietro (10.1016/j.matpr.2022.09.006_b0010) 2019; 3 Fan (10.1016/j.matpr.2022.09.006_b0060) 2020; 195 |
| References_xml | – volume: 7 year: 2021 ident: b0035 article-title: Application of machine learning in 3D bioprinting: focus on development of big data and digital twin publication-title: Int. J. Bioprinting – reference: Bank, D., Koenigstein, N., & Giryes, R. (2020). Autoencoders. – start-page: 1933 year: 2017 end-page: 1941 ident: b0055 article-title: October). Spatio-temporal autoencoder for video anomaly detection publication-title: In ProceedIngs of the 25th ACM International Conference on Multimedia – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: b0025 article-title: Deep learning publication-title: Nature – volume: 22 start-page: 100914 year: 2021 ident: b0050 article-title: Coupling machine learning with 3D bioprinting to fast track optimisation of extrusion printing publication-title: Appl. Mater. Today – reference: Srivastava, N., Mansimov, E., & Salakhudinov, R. (2015, June). Unsupervised learning of video representations using lstms. In International conference on machine learning (pp. 843-852). PMLR. – reference: (Vol. 4861, pp. 325-332). International Society for Optics and Photonics. https://doi.org/10.1117/12.456333. – volume: 13 start-page: 179 year: 2017 end-page: 200 ident: b0015 article-title: Evaluation of bioprinter technologies publication-title: Addit. Manuf. – volume: 6 year: 2020 ident: b0030 article-title: A perspective on using machine learning in 3D bioprinting publication-title: Int. J. Bioprinting – reference: Bonatti, A. F., Fortunato, G. M., Lapomarda A., De Acutis, A., De Maria, C., Vitale-Brovarone, C., Vozzi, G., (2021). Advanced Firmware and Hardware for Multiscale and Multimaterial Bioprinting, Proceedings of the Seventh National Congress of Bioengineering – start-page: 123 year: 2015 end-page: 152 ident: b0005 publication-title: Extrusion Bioprinting – volume: 9 start-page: 40511 year: 2021 end-page: 40520 ident: b0095 article-title: ResNet autoencoders for unsupervised feature learning from high-dimensional data: Deep models resistant to performance degradation publication-title: IEEE Access – volume: 24 year: 2021 ident: b0020 article-title: Open-source CAD-CAM simulator of the extrusion-based bioprinting process publication-title: Bioprinting – volume: 195 year: 2020 ident: b0060 article-title: Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder publication-title: Comput. Vis. Image Underst. – year: 2021 ident: b0045 article-title: Monitoring anomalies in 3D bioprinting with deep neural networks publication-title: ACS Biomater. Sci. Eng. – reference: arXiv preprint arXiv:2003.05991. https://doi.org/10.48550/arXiv.2003.05991. – volume: 3 year: 2019 ident: b0010 article-title: European regulatory framework for the clinical translation of bioprinted scaffolds and tissues publication-title: Biomed. Sci. Eng. – volume: 7 start-page: 343 year: 2021 ident: b0040 article-title: Evaluation of Printing Parameters on 3D Extrusion Printing of Pluronic Hydrogels and Machine Learning Guided Parameter Recommendation publication-title: International journal of bioprinting – reference: Zhao, M., Bu, J., & Chen, C. (2002, December). Robust background subtraction in HSV color space. In – start-page: 770 year: 2016 end-page: 778 ident: b0090 article-title: Deep residual learning for image recognition publication-title: In ProceedIngs of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 12 start-page: 025013 year: 2020 ident: b0075 article-title: Endothelial cells support osteogenesis in an in vitro vascularized bone model developed by 3D bioprinting publication-title: Biofabrication – reference: Multimedia systems and Applications V – volume: 7 issue: 1 year: 2021 ident: 10.1016/j.matpr.2022.09.006_b0035 article-title: Application of machine learning in 3D bioprinting: focus on development of big data and digital twin publication-title: Int. J. Bioprinting doi: 10.18063/ijb.v7i1.342 – volume: 12 start-page: 025013 issue: 2 year: 2020 ident: 10.1016/j.matpr.2022.09.006_b0075 article-title: Endothelial cells support osteogenesis in an in vitro vascularized bone model developed by 3D bioprinting publication-title: Biofabrication doi: 10.1088/1758-5090/ab6a1d – ident: 10.1016/j.matpr.2022.09.006_b0080 – volume: 3 issue: s3 year: 2019 ident: 10.1016/j.matpr.2022.09.006_b0010 article-title: European regulatory framework for the clinical translation of bioprinted scaffolds and tissues publication-title: Biomed. Sci. Eng. doi: 10.4081/bse.2019.108 – start-page: 770 year: 2016 ident: 10.1016/j.matpr.2022.09.006_b0090 article-title: Deep residual learning for image recognition – volume: 24 year: 2021 ident: 10.1016/j.matpr.2022.09.006_b0020 article-title: Open-source CAD-CAM simulator of the extrusion-based bioprinting process publication-title: Bioprinting doi: 10.1016/j.bprint.2021.e00172 – volume: 7 start-page: 343 issue: 4 year: 2021 ident: 10.1016/j.matpr.2022.09.006_b0040 article-title: Evaluation of Printing Parameters on 3D Extrusion Printing of Pluronic Hydrogels and Machine Learning Guided Parameter Recommendation publication-title: International journal of bioprinting – volume: 195 year: 2020 ident: 10.1016/j.matpr.2022.09.006_b0060 article-title: Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder publication-title: Comput. Vis. Image Underst. doi: 10.1016/j.cviu.2020.102920 – ident: 10.1016/j.matpr.2022.09.006_b0070 – volume: 9 start-page: 40511 year: 2021 ident: 10.1016/j.matpr.2022.09.006_b0095 article-title: ResNet autoencoders for unsupervised feature learning from high-dimensional data: Deep models resistant to performance degradation publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3064819 – year: 2021 ident: 10.1016/j.matpr.2022.09.006_b0045 article-title: Monitoring anomalies in 3D bioprinting with deep neural networks publication-title: ACS Biomater. Sci. Eng. – ident: 10.1016/j.matpr.2022.09.006_b0085 doi: 10.1117/12.456333 – volume: 13 start-page: 179 year: 2017 ident: 10.1016/j.matpr.2022.09.006_b0015 article-title: Evaluation of bioprinter technologies publication-title: Addit. Manuf. – volume: 22 start-page: 100914 year: 2021 ident: 10.1016/j.matpr.2022.09.006_b0050 article-title: Coupling machine learning with 3D bioprinting to fast track optimisation of extrusion printing publication-title: Appl. Mater. Today doi: 10.1016/j.apmt.2020.100914 – start-page: 1933 year: 2017 ident: 10.1016/j.matpr.2022.09.006_b0055 article-title: October). Spatio-temporal autoencoder for video anomaly detection – start-page: 123 year: 2015 ident: 10.1016/j.matpr.2022.09.006_b0005 – volume: 6 issue: 1 year: 2020 ident: 10.1016/j.matpr.2022.09.006_b0030 article-title: A perspective on using machine learning in 3D bioprinting publication-title: Int. J. Bioprinting doi: 10.18063/ijb.v6i1.253 – ident: 10.1016/j.matpr.2022.09.006_b0065 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.matpr.2022.09.006_b0025 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 |
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| Title | A deep learning approach for error detection and quantification in extrusion-based bioprinting |
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