Abstract
Organoid, a 3D in vitro cell culture, has high similarities with derived tissues or organs in vivo, which makes it widely used in personalized drug screening. Although organoids play an essential role in drug screening, the existing methods are difficult to accurately evaluate the viability of organoids, making the existing methods still have many limitations in robustness and accuracy. Determination of Adenosine triphosphate (ATP) is a mature way to analyze cell viability, which is commonly used in drug screening. However, ATP bioluminescence technique has an inherent flaw. All living cells will be lysed during ATP determination. Therefore, ATP bioluminescence technique is an end-point method, which only assess cell viability in the current state and unable to evaluate the change trend of cell viability before or after medication. In this paper, we propose a deep learning based framework, OrgaNet, for organoids viability evaluation based on organoid images. It is a straightforward and repeatable solution to evaluate organoid viability, promoting the reliability of drug screening. The OrgaNet consists of three parts: a feature extractor, extracts the representation of organoids; a multi-head classifier, improves feature robustness through supervised learning; a scoring function, measures organoids viability through contrastive learning. Specifically, to optimize our proposed OrgaNet, we constructed the first dedicated dataset, which is annotated by seven experienced experts. Experiments demonstrate that the OrgaNet shows great potential in organoid viability evaluation. The OrgaNet provides another solution to evaluate organoids viability and shows a high correlation compared with ATP bioluminescence technique.
Availability: https://github.com/541435721/OrgaNet
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bian, X., Li, G., et al.: A deep learning model for detection and tracking in high-throughput images of organoid. Comput. Biol. Med. 134, 104490 (2021)
Chen, Z., Ma, N., et al.: Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition. Biomaterials 272, 120770 (2021)
Christ, P.E., et al.: Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 415–423. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_48
Cunningham, A.C., Goh, H.P., et al.: Treatment of COVID-19: old tricks for new challenges (2020)
Emami, J., et al.: In vitro-in vivo correlation: from theory to applications. J. Pharm. Pharm. Sci. 9(2), 169–189 (2006)
Ghosh, A., Kumar, H., Sastry, P.: Robust loss functions under label noise for deep neural networks. In: AAAI, vol. 31 (2017)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Kuhlman, B., Bradley, P.: Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol. 20(11), 681–697 (2019)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Maehara, Y., Anai, H., et al.: The ATP assay is more sensitive than the succinate dehydrogenase inhibition test for predicting cell viability. Eur. J. Cancer Clin. Oncol. 23(3), 273–276 (1987)
Malach, E., Shalev-Shwartz, S.: Decoupling “when to update” from “how to update”. In: NIPS, pp. 960–970 (2017)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Polli, J.E., et al.: Novel approach to the analysis of in vitro-in vivo relationships. J. Pharm. Sci. 85(7), 753–760 (1996)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)
Rios, A.C., Clevers, H.: Imaging organoids: a bright future ahead. Nat. Meth. 15(1), 24–26 (2018)
Rossi, G., et al.: Progress and potential in organoid research. Nat. Rev. Genet. 19(11), 671–687 (2018)
Saito, K., Kim, D., Sclaroff, S., et al.: Semi-supervised domain adaptation via minimax entropy. In: ICCV, pp. 8050–8058 (2019)
Shen, C., Wang, Z., Zhao, F., et al.: Treatment of 5 critically ill patients with COVID-19 with convalescent plasma. JAMA 323(16), 1582–1589 (2020)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
Sukhbaatar, S., Bruna, J., Paluri, M., et al.: Training convolutional networks with noisy labels (2014)
Szegedy, C., Liu, W., et al.: Going deeper with convolutions. In: CVPR, pp. 1–9 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818–2826 (2016)
Yosinski, J., Clune, J., et al.: How transferable are features in deep neural networks? In: NIPS, pp. 3320–3328 (2014)
Acknowledgements
This work is supported by China Postdoctoral Science Foundation (No. 2021M690094) and the China Fundamental Research Funds for the Central Universities (No. 20720210074).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bian, X. et al. (2021). OrgaNet: A Deep Learning Approach for Automated Evaluation of Organoids Viability in Drug Screening. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-91415-8_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91414-1
Online ISBN: 978-3-030-91415-8
eBook Packages: Computer ScienceComputer Science (R0)