Semantic segmentation of pyramidal neuron skeletons using geometric deep learning

Neurons can be abstractly represented as skeletons due to the filament nature of neurites. With the rapid development of imaging and image analysis techniques, an increasing amount of neuron skeleton data is being produced. In some scientific studies, it is necessary to dissect the axons and dendrit...

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Veröffentlicht in:Journal of innovative optical health science 2023-11, Vol.16 (6)
Hauptverfasser: Li, Lanlan, Qi, Jing, Geng, Yi, Wu, Jingpeng
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Sprache:eng
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Zusammenfassung:Neurons can be abstractly represented as skeletons due to the filament nature of neurites. With the rapid development of imaging and image analysis techniques, an increasing amount of neuron skeleton data is being produced. In some scientific studies, it is necessary to dissect the axons and dendrites, which is typically done manually and is both tedious and time-consuming. To automate this process, we have developed a method that relies solely on neuronal skeletons using Geometric Deep Learning (GDL). We demonstrate the effectiveness of this method using pyramidal neurons in mammalian brains, and the results are promising for its application in neuroscience studies.
ISSN:1793-5458
1793-7205
DOI:10.1142/S1793545823400060