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NeuroLink: Bridging Weak Signals in Neuronal Imaging with Morphology Learning

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 15008))

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Abstract

Reconstructing neurons from large-scale optical microscope images is a challenging task due to the complexity of neuronal structures and extremely weak signals in certain regions. Traditional segmentation models, built on vanilla convolutions and voxel-wise losses, struggle to model long-range relationships in sparse volumetric data. As a result, weak signals in the feature space get mixed with noise, leading to interruptions in segmentation and premature termination in neuron tracing results. To address this issue, we propose NeuroLink to add continuity constraints to the network and implicitly model neuronal morphology by utilizing multi-task learning methods. Specifically, we introduce the Dynamic Snake Convolution to extract more effective features for the sparse tubular structure of neurons and propose a easily implementable morphology-based loss function to penalize discontinuous predictions. In addition, we guide the network to leverage the morphological information of the neuron for predicting direction and distance transformation maps of neurons. Our method achieved higher recall and precision on the low-contrast Zebrafish dataset and the publicly available BigNeuron dataset. Our code is available athttps://github.com/Qingjia0226/NeuroLink.

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Acknowledgments

This work was supported by grants from the STI 2030-Major Projects (2021ZD0204500, 2021ZD0204503 to L.L.), STI 2030-Major Projects (2022ZD0211900, 2022ZD0211902 to L.S.). We are grateful for the Zebrafish dataset provided by Jiulin Du and Xufei Du from Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences.

Disclosure of Interests.The authors have no competing interests to declare that are relevant to the content of this article.

Author information

Authors and Affiliations

  1. Laboratory of Brain Atlas and Brain-inspired Intelligence, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Haiyang Yan, Hao Zhai, Jinyue Guo, Linlin Li & Hua Han

  2. School of Future Technology, University of Chinese Academy of Sciences, Beijing, 101408, China

    Haiyang Yan, Hao Zhai & Hua Han

  3. School of Artificial Intellengace, University of Chinese Academy of Sciences, Beijing, China

    Jinyue Guo

Authors
  1. Haiyang Yan

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  2. Hao Zhai

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  3. Jinyue Guo

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  4. Linlin Li

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  5. Hua Han

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Corresponding author

Correspondence toHua Han.

Editor information

Editors and Affiliations

  1. Children’s National Hospital/George Washington University, Washington, DC, USA

    Marius George Linguraru

  2. The Chinese University of Hong Kong, Hong Kong, China

    Qi Dou

  3. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  4. Imperial College London, London, UK

    Stamatia Giannarou

  5. Imperial College London, London, UK

    Ben Glocker

  6. Universitat de Barcelona, Barcelona, Spain

    Karim Lekadir

  7. Helmholtz Munich, Technical University of Munich and King’s College London, Munich, Germany

    Julia A. Schnabel

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Yan, H., Zhai, H., Guo, J., Li, L., Han, H. (2024). NeuroLink: Bridging Weak Signals in Neuronal Imaging with Morphology Learning. In: Linguraru, M.G.,et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15008. Springer, Cham. https://doi.org/10.1007/978-3-031-72111-3_44

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