Paper Info
Title
Efficient and Reliable Estimation of Cell Positions.
Abstract
Sequences of microscopic images feature the dynamics of developing embryos. Automatically tracking the cells from such sequences of images allows understanding the dynamics which a living element demands to know its cells movement, which ideally should take place in real-time. The traditional tracking pipeline starts with image acquisition, data transfer, image segmentation to separate cells from the background, and then the actual tracking step. To speed up this pipeline, we hypothesize that a process capable of predicting the cell motion according to previous observations is useful. The solution must be accurate, fast and lightweight, and be able to iterate between the various components. In this work we propose CM-Predictor, which takes advantage of previous positions of cells to estimate their motion. When estimation takes place, we can omit costly acquisition, transfer and process of images, speeding up the tracking pipeline. The designed solution monitors the error of prediction, adapting the model whenever needed. For validation, we use four different datasets with sequences of images with developing embryos. Then we compare the estimated motion vectors of CM-Predictor with traditional tracking methods. Experimental results show that CM-Predictor is able to accurately estimate the motion vectors. In fact, CM-Predictor maintains the prediction quality of other algorithms and performs faster than them.
Year
DOI
Venue
2018
10.1145/3269206.3271734
CIKM
Keywords
Field
DocType
Cell tracking, moving objects, microscopic images, developing embryo, predictive analysis
Data mining,Computer vision,Data transmission,Cell tracking,Computer science,Image segmentation,Artificial intelligence,Cells movement,Speedup
Conference
ISBN
Citations 
PageRank 
978-1-4503-6014-2
0
0.34
References 
Authors
8
3
Name
Order
Citations
PageRank
Mirela T. Cazzolato194.60
Agma J. M. Traina21024153.61
Klemens Böhm31549323.08