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Advanced NeuroGPS-Tree achieves brain-wide reconstruction of neuronal population equal to manual reconstruction level (Conference Presentation)

Paper 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, in the framework 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 and can make the dataset be fastly read into memory for the reconstruction. The manual editing module in this tool can correct the errors drawn from above automated algorithms. And thus helps to achieve the reconstruction closer to human performance. We demonstrated the features of our tool on various kinds of sparsely labelled datasets. The results indicated that without loss of the reconstruction accuracy, our tool has a 7-10 folds speed gain over the commercial software that provides the manual reconstruction.

Paper Details

Date Published: 14 March 2018
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Proc. SPIE 10481, Neural Imaging and Sensing 2018, 104810N (14 March 2018); doi: 10.1117/12.2295110
Show Author Affiliations
Tingwei Quan, Huazhong Univ. of Science and Technology (China)
Hang Zhou, Huazhong Univ. of Science and Technology (China)
Shiwei Li, Huazhong Univ. of Science and Technology (China)
Qingming Luo, Huazhong Univ. of Science and Technology (China)
Hui Gong, Huazhong Univ. of Science and Technology (China)
Anan Li, Huazhong Univ. of Science and Technology (China)
Shaoqun Zeng, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10481:
Neural Imaging and Sensing 2018
Qingming Luo; Jun Ding, Editor(s)

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