Abstract
Immunogold particle detection is a time-consuming task where a single image containing almost a thousand particles can take several hours to annotate. In this work we present a framework for the automatic detection of immunogold particles that can leverage significantly the burden of this manual task. Our proposal applies a Laplacian of Gaussian (LoG) filter to provide its detection estimates to a Stacked Denoising Autoencoder (SdA). This learning model endowed with the capability to extract higher order features provides a robust performance to our framework. For the validation of our framework, a new dataset was created. Based on our work, we determined that solely the LoG detector attained more than 74.1 % of accuracy and, when combined with a SdA the accuracy is improved by at most 11.4 %.
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References
Amaral, T., Silva, L.M., Alexandre, L.A., Kandaswamy, C., Santos, J.M., de Sá, J.M.: Using different cost functions to train stacked auto-encoders. In: 2013 12th Mexican International Conference on Artificial Intelligence (MICAI), pp. 114–120. IEEE (2013)
Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. J. Mach. Learn. Res. Proc. Track 27, 17–36 (2012)
de Chaumont, F., Dallongeville, S., Chenouard, N., Hervé, N., Pop, S., Provoost, T., Meas-Yedid, V., Pankajakshan, P., Lecomte, T., Le Montagner, Y., et al.: Icy: an open bioimage informatics platform for extended reproducible research. Nature methods 9(7), 690–696 (2012)
Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240. ACM (2006)
Fisker, R., Carstensen, J.M., Hansen, M.F., Bødker, F., Mørup, S.: Estimation of nanoparticle size distributions by image analysis. J. Nanopart. Res. 2(3), 267–277 (2000)
Sousa, R.G., Esteves, T., Rocha, S., Figueiredo, F., de Sá, J.M., Alexandre, L.A., Santos, J.M., Silva, L.M.: Transfer learning for the recognition of immunogold particles in TEM imaging. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2015. LNCS, vol. 9094, pp. 374–384. Springer, Heidelberg (2015)
Lindeberg, T.: Scale-space theory: a basic tool for analyzing structures at different scales. J. Appl. Stat. 21(2), 224–270 (1994)
Mallick, S.P., Zhu, Y., Kriegman, D.: Detecting particles in cryo-em micrographs using learned features. J. Struct. Biol. 145(1), 52–62 (2004)
Monjardino, P., Rocha, S., Tavares, A.C., Fernandes, R., Sampaio, P., Salema, R., da Câmara Machado, A.: Development of flange and reticulate wall ingrowths in maize (Zea mays L.) endosperm transfer cells. Protoplasma 250(2), 495–503 (2013)
Olivo-Marin, J.C.: Extraction of spots in biological images using multiscale products. Pattern Recogn. 35(9), 1989–1996 (2002)
Ribeiro, E., Shah, M.: Computer vision for nanoscale imaging. Mach. Vis. Appl. 17(3), 147–162 (2006)
Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: Explicit invariance during feature extraction. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 833–840 (2011)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
Woolford, D., Hankamer, B., Ericksson, G.: The laplacian of gaussian and arbitrary \(z\)-crossings approach applied to automated single particle reconstruction. J. Struct. Biol. 159(1), 122–134 (2007)
Acknowledgements
The work was financed by Portuguese funds through FCT – Fundação para a Ciência e a Tecnologia in the framework of project UID/BIM/04293/2013. This work was also financed by FEDER funds through the Programa Operacional Factores de Competitividade – COMPETE and by Portuguese funds through FCT – in the framework of the project PTDC/EIA-EIA/119004/2010. We would also like to acknowledge to FCT for funding this research through project SFRH/BD/80508/2011. Sara Rocha was supported by Grant BIIC M3.1.6/F/038/2009 from Direcção Regional de Ciência e Tecnologia and by Grant SFRH/BD/8122/2002 from FCT. We thank Dr. Roberto Salema and Dr. Paulo Monjardino for their insightful comments and to Rui Fernandes from HEMS department, and to Dr. João Relvas for aiding us with the technical knowledge for conducting this work.
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Sousa, R.G., Esteves, T., Rocha, S., Figueiredo, F., Quelhas, P., Silva, L.M. (2015). Automatic Detection of Immunogold Particles from Electron Microscopy Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_41
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DOI: https://doi.org/10.1007/978-3-319-20801-5_41
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