Abstract

The brain-wide reconstruction of neuronal population is an indispensible step towards exploring the complete structure of neuronal circuits, a central task that underlies the structure-function relation in neuroscience. Recent advances in molecular labeling and imaging techniques enable us to collect the whole mouse brain imaging dataset at cellular resolution, including the morphological information of neurons across different brain region or even the whole brain. Reconstruction of these neurons poses substantial challenges, and at presents there is no tool for high-speed achieving this reconstruction close to human performance. Here, we presented a tool for filling in the blanks. The tool mainly contains the following function modules: 3D visualization of large-scale imaging dataset, automated reconstruction of neurons, manual editing of the reconstructions at local and global scale. In this tool, on the basis of our previous tools (NeuroGPS-Tree and SparseTracer), the two identifying models were constructed for boosting the automatic level of the reconstruction. One is used to identify the weak signals from inhomogeneous backgrounds and the other is used to identify closely packed neurites. This tool can be suitable for the different big-data formats like fMOSTd and Terafly, and can make the dataset be quickly read into memory for the reconstruction. For the reconstruction that achieves the gold standard (manual reconstruction with commercial software), this tool has manual editing’s function that can correct the reconstruction errors drawn from the automated algorithms. achieved brain-wide reconstruction of neuronal population that are close to the golden standard, and about 10 times faster than reconstructions with commercial software (7-14 days for a neuron reconstruction). We further demonstrated that AdTree can be suitable for the brain imaging datasets collected with different optical systems.

© 2017 Optical Society of America