AUTHOR=Cheng Shenghua , Wang Xiaojun , Liu Yurong , Su Lei , Quan Tingwei , Li Ning , Yin Fangfang , Xiong Feng , Liu Xiaomao , Luo Qingming , Gong Hui , Zeng Shaoqun TITLE=DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale JOURNAL=Frontiers in Neuroinformatics VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2019.00025 DOI=10.3389/fninf.2019.00025 ISSN=1662-5196 ABSTRACT=

Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology. Here, we developed an automated method for recognizing single-neuron axonal boutons in whole-brain fluorescence microscopy datasets. The method is based on deep convolutional neural networks and density-peak clustering. High-dimensional feature representations of bouton morphology can be learned adaptively through convolutional networks and used for bouton recognition and subtype classification. We demonstrate that the approach is effective for detecting single-neuron boutons at the brain-wide scale for both long-range pyramidal projection neurons and local interneurons.