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
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