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Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method

  • Shiwei Li
  • Tingwei QuanEmail author
  • Hang Zhou
  • Qing Huang
  • Tao Guan
  • Yijun Chen
  • Cheng Xu
  • Hongtao Kang
  • Anan Li
  • Ling Fu
  • Qingming Luo
  • Hui Gong
  • Shaoqun Zeng
Original Article
  • 55 Downloads

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.

Keywords

Neuronal shape reconstruction Brain-wide neurite segmentation Convex image segmentation 

Notes

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.

Supplementary material

12021_2019_9434_MOESM1_ESM.docx (690 kb)
ESM 1 (DOCX 689 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Shiwei Li
    • 1
    • 2
  • Tingwei Quan
    • 1
    • 2
    • 3
    Email author
  • Hang Zhou
    • 1
    • 2
  • Qing Huang
    • 1
    • 2
  • Tao Guan
    • 4
  • Yijun Chen
    • 1
    • 2
  • Cheng Xu
    • 1
    • 2
  • Hongtao Kang
    • 1
    • 2
  • Anan Li
    • 1
    • 2
  • Ling Fu
    • 1
    • 2
  • Qingming Luo
    • 1
    • 2
  • Hui Gong
    • 1
    • 2
  • Shaoqun Zeng
    • 1
    • 2
  1. 1.Britton Chance Center for Biomedical PhotonicsWuhan National Laboratory for Optoelectronics-Huazhong University of Science and TechnologyWuhanChina
  2. 2.MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering SciencesHuazhong University of Science and TechnologyWuhanChina
  3. 3.School of Mathematics and EconomicsHubei University of EducationWuhanChina
  4. 4.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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