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DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox.
Liu, Min; Lin, Zhuangdian; Chen, Weixun; Meijering, Erik; Wang, Yaonan.
Afiliación
  • Liu M; College of Electrical and Information Engineering, Hunan University, Hunan, China. liu_min@hnu.edu.cn.
  • Lin Z; National Engineering Research Center for Robot Visual Perception and Control Technology, Changsha, China. liu_min@hnu.edu.cn.
  • Chen W; College of Electrical and Information Engineering, Hunan University, Hunan, China.
  • Meijering E; National Engineering Research Center for Robot Visual Perception and Control Technology, Changsha, China.
  • Wang Y; College of Electrical and Information Engineering, Hunan University, Hunan, China.
Methods Mol Biol ; 2831: 179-197, 2024.
Article en En | MEDLINE | ID: mdl-39134850
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagenología Tridimensional / Aprendizaje Profundo / Neuronas Límite: Animals / Humans Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagenología Tridimensional / Aprendizaje Profundo / Neuronas Límite: Animals / Humans Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos