Abstract
Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging. In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover regions, and we verified that our model could robustly reconstruct the neuron shape on a brain-wide scale.
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Acknowledgments
We thank Dr. Pavel Osten for providing testing datasets and we also thank the DIADEM and BigNeuron community for providing the public datasets. This work was supported by the Science Fund for Creative Research Group of China (Grant No. 61421064), National Program on Key Basic Research Project of China (Grant No. 2015CB7556003), National Natural Science Foundation of China (Grant No. 81327802), Science Fund for Young and Middle-aged Creative Research Group of the Universities in Hubei Province (Grant No. T201520), Natural Science Foundation of Hubei Province (2014CFB564) and Director Fund of WNLO.
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Li, S., Quan, T., Zhou, H. et al. Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method. Neuroinform 18, 199–218 (2020). https://doi.org/10.1007/s12021-019-09434-x
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DOI: https://doi.org/10.1007/s12021-019-09434-x