Paper Info
Title
Counting cells from microscopy videos without tracking individual cells
Abstract
Counting the unique number of cells in a microscopy video (i.e., counting a cell only once while the cell is within the field of view in the video), is required in many biological and pathological studies. Conventionally, cell counting from videos is computed by tracking individual cells. Because tracking cells is non-trivial, these methods are plagued with inaccuracies. In this paper, we engineer a novel and straightforward solution to the problem of unique cell counting by combining frame based cell count with simple pixel motion computation. We estimate the influx and/or the outflux rate of unique cells in a region of interest within the field of view of a microscopy video. The unique count is then obtained by summing the influx and/or the outflux rates. Our proposed framework avoids individual cell tracking altogether; thus, it is capable of overcoming various difficulties associated individual cell tracking. We validate the framework on 11 cell videos of human monocytes. The number of cells is numerous in these videos, yet we obtain a mean counting accuracy of 99%.
Year
DOI
Venue
2014
10.1109/ISBI.2014.6867909
Biomedical Imaging
Keywords
Field
DocType
cellular biophysics,biological studies,human monocytes,individual cell tracking,influx rate,microscopy video,outflux rate,pathological studies,pixel motion computation,unique cell counting
Computer vision,Cellular biophysics,Computer science,Artificial intelligence,Microscopy,Cell counting
Conference
ISSN
Citations 
PageRank 
1945-7928
1
0.35
References 
Authors
8
3
Name
Order
Citations
PageRank
Satarupa Mukherjee170.95
Ray Nilanjan254155.39
Scott T. Acton368875.19