Paper Info
Title
Shape statistics for cell division detection in time-lapse videos of early mouse embryo
Abstract
We describe a statistical approach to the problem of estimating the times of cell-division cycles in time-lapse movies of early mouse embryos. Our method is based on the likelihoods for cells of certain radii ranges to be in each frame - without actually locating or counting the cells. Computing the likelihoods consists of a voting scheme where votes come form quadruples of points in a way similar to the first step of the Randomized Hough Transform for ellipse detection. To locate divisions, we search for points of abrupt change in the matrix of likelihoods (built for all frames), and pick the two optimal division points using a dynamic programming algorithm. Our results for the first and second cell division cycles differ less than two frames from the medians of the annotated times in a database of 100 annotated videos, and outperform two other recent methods in the same set.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7025735
Image Processing
Keywords
Field
DocType
cellular biophysics,image recognition,medical image processing,Randomized Hough Transform,cell division detection,dynamic programming algorithm,early mouse embryo,ellipse detection,shape statistics,time lapse videos,division detection,mouse embryo,shape statistics,time lapse,video analysis
Randomized Hough transform,Cell division,Dynamic programming,Computer vision,Pattern recognition,Matrix (mathematics),Computer science,Artificial intelligence,Ellipse
Conference
ISSN
Citations 
PageRank 
1522-4880
0
0.34
References 
Authors
4
4
Name
Order
Citations
PageRank
Marcelo Cicconet100.34
Kristin C. Gunsalus291.19
Davi Geiger300.34
Michael Werman400.34