DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox.

Journal: Methods in molecular biology (Clifton, N.J.)
Published Date: Jan 1, 2024

Abstract

Digital reconstruction of neuronal structures from 3D neuron microscopy images is critical for the quantitative investigation of brain circuits and functions. Currently, neuron reconstructions are mainly obtained by manual or semiautomatic methods. However, these ways are labor-intensive, especially when handling the huge volume of whole brain microscopy imaging data. Here, we present a deep-learning-based neuron morphology analysis toolbox (DNeuroMAT) for automated analysis of neuron microscopy images, which consists of three modules: neuron segmentation, neuron reconstruction, and neuron critical points detection.

Authors

  • Min Liu
    Department of Critical Care Medicine, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China.
  • Zhuangdian Lin
    College of Electrical and Information Engineering, Hunan University, Hunan, China.
  • Weixun Chen
  • Erik Meijering
    Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • Yaonan Wang

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