Abstract
Orderly automatic collection of brain slices on the silicon substrate is critical for understanding the working principle of the whole-brain neural network. Accurate and real-time brain slices detection with microscopic CCD is crucial for automatic collection of brain slices. To solve this task, an efficient simplified SSD detection model with Cycle-GAN data augmentation is presented in this paper. The proposed simplified SSD streamlines the detection network of the original SSD architecture, leading to a more rapid detection. Moreover, the proposed Cycle-GAN data augmentation method overcomes the limitation of training images. To verify the effectiveness of the proposed method, experiments are conducted with a self-made brain slices dataset. The experiment results suggest that, the proposed method has a good performance of rapidly detecting brain slices with only a small training dataset.
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References
Kubota, Y.: New developments in electron microscopy for serial image acquisition of neuronal profiles. Microscopy 64, 27–36 (2015)
Mikula, S.: Progress towards Mammalian whole-brain cellular connectomics. Front. Neuroanat. 10, 62–71 (2016)
Schalek, R., et al.: Development of high-throughput, high-resolution 3D reconstruction of large-volume biological tissue using automated tape collection ultramicrotomy and scanning electron microscopy. Microsc. Microanal. 17, 966–967 (2011)
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. arXiv preprint arXiv:1711.08565 (2017)
Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., Malossi, C.: BAGAN: data augmentation with balancing GAN. arXiv preprint arXiv:1803.09655 (2018)
Mao, Y., Yin, Z., Schober, J.M.: Iteratively training classifiers for circulating tumor cell detection. In: IEEE 12th International Symposium on Biomedical Imaging, pp. 190–194 (2015)
Holmström, O., et al.: Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted Helminths and Schistosoma haematobium. Glob. Health. Action. 10, 1337325 (2017)
Yang, S., Fang, B., Tang, W., Wu, X., Qian, J., Yang, W.: Faster R-CNN based microscopic cell detection. In: International Conference on Security, Pattern Analysis, and Cybernetics, pp. 345–350 (2017)
Hung, J., Carpenter, A.: Applying faster R-CNN for object detection on malaria images. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 808–813 (2017)
Zhang, J., Hu, H., Chen, S., Huang, Y., Guan, Q.: Cancer cells detection in phase-contrast microscopy images based on Faster R-CNN. In: 9th International Symposium on Computational Intelligence and Design, pp. 363–367 (2016)
Liu, W., et al.: SSD: single shot multibox detector. In: Proceedings of the European Conference on Computer Vision, pp. 21–37 (2016)
Yi, J., Wu, P., Hoeppner, D.J., Metaxas, D.: Fast neural cell detection using light-weight SSD neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 108–112 (2017)
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 61873268, 61633016, in part by the Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program, in part by the Beijing Municipal Natural Science Foundation under Grant 4162066.
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Liu, W., Cheng, L., Meng, D. (2018). Brain Slices Microscopic Detection Using Simplified SSD with Cycle-GAN Data Augmentation. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_40
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DOI: https://doi.org/10.1007/978-3-030-04212-7_40
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