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 µm) 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)
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)
