Figure 1.

The principle of DeepBouton. (A) Flow diagram of DeepBouton: extract images along axons piecewise from a whole-brain dataset guided by a manually traced neuronal skeleton, segment foreground images by adaptive binarization and morphology erosion, initially detect underlying boutons using density-peak clustering, and filter non-bouton axonal swellings via a deep convolutional network. (B) Pattern graphs of DeepBouton corresponding to the flow diagram in (A). (C) Diagram of piecewise-extracted images along axons: the axonal arbor is divided into segments with redundancy, and the tubular volume is extracted along the axonal skeleton for each segment. (D) Diagram of initially detected boutons using density-peak clustering: the points with a higher signal density than their neighbors and with a relatively large distance from points of higher densities are recognized as centers of underlying boutons (red dots), while the points with a higher density but with a small distance are not centers (black dots labeled by arrows). (E) Filtering of non-bouton axonal swellings in the initial detection via a patch-based classification convolutional network. (F) A demonstration of the method on an experimental dataset. Scale bars in (F) represent 1 mm and 2 μm, respectively.