Semantic segmentation of pyramidal neuron skeletons using geometric deep learning

作     者：Lanlan Li Jing Qi Yi Geng Jingpeng Wu 

作者机构：Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou UniversityFuzhouFujian 350116P.R.China Center for Computational Neuroscience Flatiron InstituteNew York 10010USA 

出 版 物：《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志（英文）)

年 卷 期：2023年第16卷第6期

页      面：69-76页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：Pyramidal neuron geometric deep learning neuron skeleton semantic segmentation point cloud. 

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.