Abstract
This paper developed and evaluated a method for the detection of spots in microscopy images. Spots are subcellular particles formed as a result of biomarkers tagged to biomolecules in a specimen and observed via fluorescence microscopy as bright spots. Various approaches that automatically detect spots have been proposed to improve the analysis of biological images. The proposed spot detection method named, detectSpot includes the following steps: (1) A convolutional neural network is trained on image patches containing single spots. This trained network will act as a classifier to the next step. (2) Apply a sliding-window on images containing multiple spots, classify and accept all windows with a score above a given threshold. (3) Perform post-processing on all accepted windows to extract spot locations, then, (4) finally, suppress overlapping detections which are caused by the sliding window-approach. The proposed method was evaluated on realistic synthetic images with known and reliable ground truth. The proposed approach was compared to two other popular CNNs namely, GoogleNet and AlexNet and three traditional methods namely, Isotropic Undecimated Wavelet Transform, Laplacian of Gaussian and Feature Point Detection, using two types of synthetic images. The experimental results indicate that the proposed methodology provides fast spot detection with precision, recall and F_score values that are comparable to GoogleNet and higher compared to other methods in comparison. Statistical test between detectSpot and GoogleNet shows that the difference in performance between them is insignificant. This implies that one can use either of these two methods for solving the problem of spot detection.
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
Similar content being viewed by others
References
Bashir, A., Mustafa, Z.A., Abdelhameid, I., Ibrahem, R.: Detection of malaria parasites using digital image processing. In: Proceedings of the International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), Khartoum (2017)
Verma, A., Khanna, G.: Survey on digital image processing techniques for tumor detection. Indian J. Sci. Technol. 9(14), 1–15 (2016)
Mabaso, M., Withey, D., Twala, B.: Spot detection in microscopy images using convolutional neural network with sliding-window approach. In: Proceedings of the 5th International Conference on Bioimaging, Funchal-Madeira (2018)
Varga, D., Szirányi, T.: Detecting pedestrians in surveillance videos based on convolutional neural network and motion. In: 24th European Signal Processing Conference (EUSIPCO), Budapest, Hungary (2016)
Wang, Z., Li, Z., Wang, B., Liu, H.: Robot grasp detection using multimodal deep convolutional neural networks. Adv. Mech. Eng. 8(9), 1–12 (2016)
Li, R., et al.: Deep learning based imaging data completion for improved brain disease diagnosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City (2014)
Genovesio, A., Liendl, T., Emiliana, V., Parak, W.J., Coppey-Moisan, M., Olivo-Marin, J.-C.: Multiple particle tracking in 3D+t microscopy: method and application to the tracking of endocytosed quantum dots. IEEE Trans. Image Process. 15(5), 1062–1070 (2006)
Olivo-Marin, J.-C.: Extraction of spots in biological images using multiscale products. Pattern Recogn. 35(9), 1989–1996 (2002)
Kimori, Y., Baba, N., Morone, N.: Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images. BMC Bioinf. 11(373), 1–13 (2010)
Smal, I., Loog, M., Niessen, W., Meijering, E.: Quantitative comparison of spot detection methods in fluorescence microscopy. IEEE Trans. Med. Imaging 29(2), 282–301 (2010)
Mabaso, M., Withey, D., Twala, B.: Spot detection methods in fluorescence microscopy imaging: a review. Image Anal. Stereol. 37(3), 173–190 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: arXiv:1512.03385 (2015)
Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: IEEE International Conference on Computer Vision (2015)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet Classification with deep convolutional neural networks. In: Neural Information Processing Systems (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition, Boston (2015)
Van Valen, D.A., et al.: Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput. Biol. 12(11), 1–24 (2016)
Mabaso, M., Withey, D., Twala, B. Spot detection in microscopy images using convolutional neural network with sliding-window approach. In: Proceedings of the 5th International Conference on Bioimaging, Funchal (2018)
Ruder, S.: An overview of gradient descent optimization algorithms (2017). http://ruder.io/optimizing-gradient-descent/. Accessed 10 Oct 2017
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (2010)
Damien, A.: TFLearn, GitHub (2016)
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous system. In: 12th USENIX Symposium on Operating Systems Design and Implementation (2015)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference for Learning Representations, San Diego (2015)
Mabaso, M., Withey, D., Twala, B.: A framework for creating realistic synthetic fluorescence microscopy image sequences. In: Bioimaging 2016, Rome (2016)
Chenouard, N.: Particle tracking benchmark generator. Institut Pasteur (2015). http://icy.bioimageanalysis.org/plugin/Particle_tracking_benchmark_generator. Accessed 1 Nov 2016
The open microscopy environment (2016). http://www.openmicroscopy.org/site/support/omero5.2/developers/Matlab.html. Accessed 15 Nov 2016
Sbalzarini, I.F., Koumoutsakos, P.: Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol. 151(2), 182–195 (2005)
Olivo-Marin, J.-C.: Extraction of spots in biological images using multiscale products. Pattern Recognit. 35(9), 1989–1996 (2002)
Raj, A., van den Bogaard, P., Rifkin, S.A., van Oudenaarfen, A., Tyagi, S.: Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5(10), 877–879 (2008)
Acknowledgements
This work was carried out in financial support from the Council for Scientific and Industrial Research (CSIR) and the Electrical and Electronic Engineering Department at the University of Johannesburg.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mabaso, M., Withey, D., Twala, B. (2019). A Convolutional Neural Network for Spot Detection in Microscopy Images. In: Cliquet Jr., A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2018. Communications in Computer and Information Science, vol 1024. Springer, Cham. https://doi.org/10.1007/978-3-030-29196-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-29196-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29195-2
Online ISBN: 978-3-030-29196-9
eBook Packages: Computer ScienceComputer Science (R0)