Automatic Identification of the Activity Area in Brain with Deep Neural Networks

Abstract:

We use deep max-pooling convolutional neural networks to address a problem of neuroanatomy, namely, the automatic segmentation of cerebral cortex structures of laboratory rat depicted in stacks of Two-photon microscopy images and detect the change areas when stimulation occurs. We classify each pixel in the image by training a CNN network, using a square window to predict the probability of the central pixel for each class. After classification, we perform the post-processing on the output produced by CNN. At last, we depict the areas that we interested through a threshold value.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 4941-4944
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.4941
• Online since: May 2014
• Authors: Li Xiang Shi*, Li Peng, Lu Lu Yue, Zhi Xing Huang
• Keywords: Activity Area in Brain, Deep Neural Network, Two-Photon Microscopy

References

[1] The Open Connectome Project. http: /openconnectomeproject. org.

[2] Dan Claudiu Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella, and J¨ urgen Schmidhuber. Flexible, high performance convolutional neural networks for image classification. In International Joint Conference on Artificial Intelligence, pages 1237–1242, (2011).

DOI: 10.1109/ijcnn.2011.6033458

[3] Dan Claudiu Ciresan, Ueli Meier, and J¨ urgen Schmidhuber. Multi-column deep neural networks for image classification. In Computer Vision and Pattern Recognition, pages 3642–3649, (2012).

DOI: 10.1109/cvpr.2012.6248110

[4] Fukushima, K.: Neocognitron: A self-organizing neural network for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980).

DOI: 10.1007/bf00344251

[5] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278–2324, November (1998).

DOI: 10.1109/5.726791

[6] Maximiliam Riesenhuber and Tomaso Poggio. Hierarchical models of object recognition in cortex. Nat. Neurosci., 2(11): 1019–1025, (1999).

DOI: 10.1038/14819

[7] .

[31] Dominik Scherer, Adreas M¨ uller, and Sven Behnke. Evaluation of pooling operations in convolutional architectures for object recognition. In International Conference on Artificial Neural Networks, (2010).

DOI: 10.1007/978-3-642-15825-4_10

[8] .

[32] Thomas Serre, Lior Wolf, and Tomaso Poggio. Object recognition with features inspired by visual cortex. In Proc. of Computer Vision and Pattern Recognition Conference, (2005).

DOI: 10.1109/cvpr.2005.254