Abstract
Microplastics (MP) have become a major concern, given the threat they pose to marine-derived food and human health. One way to investigate this threat is to quantify MP found in marine organisms, for instance making use of image analysis to identify ingested MP in fluorescent microscopic images. In this study, we propose a deep learning-based segmentation model to generate binarized images (masks) that make it possible to clearly separate MP from other background elements in the aforementioned type of images. Specifically, we created three variants of the U-Net model with a ResNet-101 encoder, training these variants with 99 high-resolution fluorescent images containing MP, each having a mask that was generated by experts using manual color threshold adjustments in ImageJ. To that end, we leveraged a sliding window and random selection to extract patches from the high-resolution images, making it possible to adhere to input constraints and to increase the number of labeled examples. When measuring effectiveness in terms of accuracy, recall, and F\(_{2}\)-score, all segmentation models exhibited low scores. However, compared to two ImageJ baseline methods, the effectiveness of our segmentation models was better in terms of precision, F\(_{0.5}\)-score, F\(_{1}\)-score, and mIoU: U-Net (1) obtained the highest mIoU of 0.559, U-Net (2) achieved the highest F\(_{1}\)-score of 0.682, and U-Net (3) had the highest precision and F\(_{0.5}\)-score of 0.594 and 0.626, respectively, with our segmentation models, in general, detecting less false positives in the predicted masks. In addition, U-Net (1), which used binary cross-entropy loss and stochastic gradient descent, and U-Net (2), which used dice loss and Adam, were most effective in discriminating MP from other background elements. Overall, our experimental results suggest that U-Net (1) and U-Net (2) allow for more effective MP identification and measurement than the macros currently available in ImageJ.
J. Y. Baek, M. K. de Guzman, H. Park, and S. Park—Contributed equally.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anbumani, S., Kakkar, P.: Ecotoxicological effects of microplastics on biota: a review. Environ. Sci. Pollut. Res. 25(15), 14373–14396 (2018). https://doi.org/10.1007/s11356-018-1999-x
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv preprint arXiv:1706.05587 (2017)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Cole, M., Lindeque, P., Halsband, C., Galloway, T.S.: Microplastics as contaminants in the marine environment: a review. Mar. Pollut. Bull. 62(12), 2588–2597 (2011)
Cressey, D.: The plastic ocean. Nature 536(7616), 263–265 (2016)
Dehaut, A., et al.: Microplastics in seafood: benchmark protocol for their extraction and characterization. Environ. Pollut. 215, 223–233 (2016)
Deng, R., Shen, C., Liu, S., Wang, H., Liu, X.: Learning to predict crisp boundaries. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 570–586. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_35
Erni-Cassola, G., Gibson, M.I., Thompson, R.C., Christie-Oleza, J.A.: Lost, but found with nile red: a novel method for detecting and quantifying small microplastics (1 mm to 20 \(\varvec {\upmu }\)m) in environmental samples. Environ. Sci. Technol 51(23), 13641–13648 (2017)
Galloway, T.S.: Micro- and nano-plastics and human health. In: Bergmann, M., Gutow, L., Klages, M. (eds.) Mar. Anthropogenic Litter, pp. 343–366. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16510-3_13
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Isobe, A., Kubo, K., Tamura, Y., Nakashima, E., Fujii, N., et al.: Selective transport of microplastics and mesoplastics by drifting in coastal waters. Mar. Pollut. Bull. 89(1–2), 324–330 (2014)
Jadon, S.: A survey of loss functions for semantic segmentation. arXiv preprint arXiv:2006.14822 (2020)
Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014)
Maes, T., Jessop, R., Wellner, N., Haupt, K., Mayes, A.G.: A rapid-screening approach to detect and quantify microplastics based on fluorescent tagging with Nile Red. Sci. Rep. 7(1), 1–10 (2017)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image Segmentation Using Deep Learning: A Survey. arXiv preprint arXiv:2001.05566 (2020)
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT press, Cambridge (2012)
Prata, J.C., Alves, J.R., da Costa, J.P., Duarte, A.C., Rocha-Santos, T.: Major factors influencing the quantification of Nile Red stained microplastics and improved automatic quantification (MP-VAT 2.0). Sci. Total Environ. 719, 137498 (2020)
Prata, J.C., Reis, V., Matos, J.T., da Costa, J.P., Duarte, A.C., Rocha-Santos, T.: A new approach for routine quantification of microplastics using Nile Red and automated software (MP-VAT). Sci. Total Environ. 690, 1277–1283 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Rényi, A.: On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability: Contributions to the Theory of Statistics, vol. 1, pp. 547–561. The Regents of the University of California, University of California Press (1961)
Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675 (2012)
Silva, A.B., Bastos, A.S., Justino, C.I., da Costa, J.P., Duarte, A.C., Rocha-Santos, T.A.: Microplastics in the environment: challenges in analytical chemistry - a review. Analytica Chimica Acta 1017, 1–19 (2018)
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_28
Wesch, C., Bredimus, K., Paulus, M., Klein, R.: Towards the suitable monitoring of ingestion of microplastics by marine biota: A review. Environ. Pollut. 218, 1200–1208 (2016)
Yakubovskiy, P.: Segmentation Models (2019). https://github.com/qubvel/segmentation_models
Yan, F., Zhang, H., Kube, C.R.: A multistage adaptive thresholding method. Pattern Recogn. Lett 26(8), 1183–1191 (2005).https://doi.org/10.1016/j.patrec.2004.11.003, http://www.sciencedirect.com/science/article/pii/S0167865504003290
Acknowledgements
The research and development activities described in this paper were funded by Ghent University Global Campus (GUGC) and by the Special Research Fund (BOF) of Ghent University (grant no. 01N01718).
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
Baek, J.Y. et al. (2021). Developing a Segmentation Model for Microscopic Images of Microplastics Isolated from Clams. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-68780-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68779-3
Online ISBN: 978-3-030-68780-9
eBook Packages: Computer ScienceComputer Science (R0)