Abstract
The synapse, which is the carrier of neurotransmitter molecules to transmit and store information, is believed to be the key to the reconstruction of the neural circuit. To date, electron microscope (EM) is considered as one of the most important tools for observing and analyzing synaptic structures because they can clearly observe the internal structure of cells. Consequently, many meaningful researches are focused on how to detect and segment the synapses from EM images. In this paper, we propose a novel and effective method to automatically detect and segment the synaptic clefts by using Mask R-CNN. On this base, we utilize the context cues in adjacent sections to eliminate the misleading results. We apply the method to the CREMI challenge and the results demonstrate that our method is effective in segmenting the synaptic clefts of the drosophila. Specifically, we rank first in sample B+ dataset, and the CREMI score is 86.50 which outperforms most of state-of-the-art methods by a large margin.
This paper is supported by National Science Foundation of China (No. 61673381, No. 61201050, No. 61701497, No. 11771130), Scientific Instrument Developing Project of Chinese Academy of Sciences (No. YZ201671), Bureau of International Cooperation, CAS (No. 153D31KYSB20170059), and Special Program of Beijing Municipal Science & Technology Commission (No. Z161100000216146).
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Hong, B., Liu, J., Li, W., Xiao, C., Xie, Q., Han, H. (2018). Fully Automatic Synaptic Cleft Detection and Segmentation from EM Images Based on Deep Learning. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_7
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DOI: https://doi.org/10.1007/978-3-030-00563-4_7
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