Semantic segmentation of pyramidal neuron skeletons using geometric deep learning  

作　　者：Lanlan Li Jing Qi Yi Geng Jingpeng Wu 

机构地区：[1]Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University,Fuzhou,Fujian 350116,P.R.China [2]Center for Computational Neuroscience Flatiron Institute,New York 10010,USA

出　　处：《Journal of Innovative Optical Health Sciences》2023年第6期69-76,共8页创新光学健康科学杂志（英文）

基　　金：supported by the Simons Foundation,the National Natural Science Foundation of China(No.NSFC61405038);the Fujian provincial fund(No.2020J01453).

摘　　要：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 scienti fic 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.

关 键 词：Pyramidal neuron geometric deep learning neuron skeleton semantic segmentation point cloud. 

分 类 号：R318[医药卫生—生物医学工程]