Abstract
Efficient and accurate digital reconstruction of neurons from large-scale 3D microscopic images remains a challenge in neuroscience. We propose a new automatic 3D neuron reconstruction algorithm, TReMAP, which utilizes 3D Virtual Finger (a reverse-mapping technique) to detect 3D neuron structures based on tracing results on 2D projection planes. Our fully automatic tracing strategy achieves close performance with the state-of-the-art neuron tracing algorithms, with the crucial advantage of efficient computation (much less memory consumption and parallel computation) for large-scale images.
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Acknowledgments
We thank Nuno da Costa, Staci Sorensen, Julie Harris, Raina D’Aleo, and Soumya Chatterjee for providing the images of mouse neurons, Paloma Gonzalez-Bellido for providing the images of dragonfly neurons, Hanbo Chen and Yujie Li for comments. This work is supported by the Allen Institute for Brain Science.
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This method has been implemented as an Open Source plugin for Vaa3D (http://vaa3d.org).
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Zhou, Z., Liu, X., Long, B. et al. TReMAP: Automatic 3D Neuron Reconstruction Based on Tracing, Reverse Mapping and Assembling of 2D Projections. Neuroinform 14, 41–50 (2016). https://doi.org/10.1007/s12021-015-9278-1
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DOI: https://doi.org/10.1007/s12021-015-9278-1