Imaging data analysis using non-negative matrix factorization

被引：8
|
作者：
Aonishi, Toru [1 ,2 ,3 ]
Maruyama, Ryoichi [2 ]
Ito, Tsubasa [2 ,3 ]
Miyakawa, Hiroyoshi [5 ]
Murayama, Masanori [3 ]
Ota, Keisuke [3 ,4 ]
机构：
[1] Tokyo Inst Technol, Sch Comp, Kanagawa, Kanagawa, Japan
[2] Tokyo Inst Technol, Interdisciplinary Grad Sch Sci & Engn, Kanagawa, Kanagawa, Japan
[3] RIKEN Ctr Brain Sci, Saitama, Japan
[4] Univ Tokyo, Grad Sch Med, Tokyo, Japan
[5] Tokyo Univ Pharm & Life Sci, Sch Life Sci, Tokyo, Japan
来源：
NEUROSCIENCE RESEARCH | 2022年 / 179卷
基金：
日本科学技术振兴机构;
关键词：
Multicellular calcium imaging; Wide field-of-view microscope; Region of interest; Cell detection; Machine learning;
D O I：
10.1016/j.neures.2021.12.001
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
The rapid progress of imaging devices such as two-photon microscopes has made it possible to measure the activity of thousands to tens of thousands of cells at single-cell resolution in a wide field of view (FOV) data. However, it is not possible to manually identify thousands of cells in such wide FOV data. Several research groups have developed machine learning methods for automatically detecting cells from wide FOV data. Many of the recently proposed methods using dynamic activity information rather than static morphological information are based on non-negative matrix factorization (NMF). In this review, we outline cell-detection methods related to NMF. For the purpose of raising issues on NMF cell detection, we introduce our current development of a nonNMF method that is capable of detecting about 17,000 cells in ultra-wide FOV data.
引用
收藏
页码：51 / 56
页数：6
相关论文
共 50 条
  • [41] Diverse Non-Negative Matrix Factorization for Multiview Data Representation
    Wang, Jing
    Tian, Feng
    Yu, Hongchuan
    Liu, Chang Hong
    Zhan, Kun
    Wang, Xiao
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2018, 48 (09) : 2620 - 2632
  • [42] The non-negative matrix factorization toolbox for biological data mining
    Li, Yifeng
    Ngom, Alioune
    [J]. SOURCE CODE FOR BIOLOGY AND MEDICINE, 2013, 8 (01)
  • [43] Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering
    Chen, Yanhua
    Wang, Lijun
    Dong, Ming
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2010, 22 (10) : 1459 - 1474
  • [44] NON-NEGATIVE MATRIX FACTORIZATION OF CLUSTERED DATA WITH MISSING VALUES
    Chen, Rebecca
    Varshney, Lav R.
    [J]. 2019 IEEE DATA SCIENCE WORKSHOP (DSW), 2019, : 180 - 184
  • [45] Application of non-negative matrix factorization to LC/MS data
    Rapin, Jeremy
    Souloumiac, Antoine
    Bobin, Jerome
    Larue, Anthony
    Junot, Chistophe
    Ouethrani, Minale
    Starck, Jean-Luc
    [J]. SIGNAL PROCESSING, 2016, 123 : 75 - 83
  • [46] Filtering Wind in Infrasound Data by Non-Negative Matrix Factorization
    Carniel, Roberto
    Cabras, Giuseppe
    Ichihara, Mie
    Takeo, Minoru
    [J]. SEISMOLOGICAL RESEARCH LETTERS, 2014, 85 (05) : 1056 - 1062
  • [47] Kernel Joint Non-Negative Matrix Factorization for Genomic Data
    Salazar, Diego
    Rios, Juan
    Aceros, Sara
    Florez-Vargas, Oscar
    Valencia, Carlos
    [J]. IEEE ACCESS, 2021, 9 : 101863 - 101875
  • [48] Sparse non-negative matrix factorization for uncertain data clustering
    Chen, Danyang
    Wang, Xiangyu
    Xu, Xiu
    Zhong, Cheng
    Xu, Jinhui
    [J]. INTELLIGENT DATA ANALYSIS, 2022, 26 (03) : 615 - 636
  • [49] PIANO SOUND ANALYSIS USING NON-NEGATIVE MATRIX FACTORIZATION WITH INHARMONICITY CONSTRAINT
    Rigaud, Francois
    David, Bertrand
    Daudet, Laurent
    [J]. 2012 PROCEEDINGS OF THE 20TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2012, : 2462 - 2466
  • [50] Time Series Electricity Consumption Analysis using Non-negative Matrix Factorization
    Gakushuin University, Computer Centre, Tokyo, Japan
    不详
    [J]. IEEE Int. Conf. Aware. Sci. Technol., iCAST - Proc., 2019,
12345