Probabilistic Model‐Based Cell Tracking

Abstract

The study of cell behavior is of crucial importance in drug and disease research. The fields of bioinformatics and biotechnology rely on the collection, processing, and analysis of huge numbers of biocellular images, including cell features such as cell size, shape, and motility. However manual methods of inferring these values are so onerous that automated methods of cell tracking and segmentation are in high demand. In this paper, a novel model-based cell tracker is designed to locate and track individual cells. The proposed cell tracker has been successfully applied to track hematopoietic stem cells (HSCs) based on identified cell locations and probabilistic data association.

Context aware spatio-temporal cell tracking in densely packed multilayer tissues

“…Besides the aforementioned approaches, tracking based on association between detections such as (Kachouie et al, 2006;Kirubarajan et al, 2001) has shown good performance on timelapse images. In (Bise et al, 2011), the authors proposed a cell tracking method on phase contrast time-lapse images that performs a global association of tracklets generated by frame-byframe detection based tracking.…”

Cell Image Processing Methods for Automatic Cell Pattern Recognition and Morphological Analysis of Mesenchymal Stem Cells - An Algorithm for Cell Classification and Adaptive Brightness Correction -

“…Kachouie et al (2007) proposed a deconvolution method in the form of an optimized ellipse fitting algorithm to locate individuals hematopoietic stem cells. The methods proposed in (Kachouie et al, 2006;Kachouie et al, 2005) uses the cell morphologic information (e. g. cell size, boundary brightness, interior brightness and boundary uniformity or symmetry) to locate and track hematopoietic stem cells. Importantly the works cited above handled with only one type of stem cell in their images.…”

Circle Detection Using a Gabor Annulus

“…This process may be infeasibly time consuming when counting cells manually for large numbers of experiments [6,23]. A further more advanced application is the tracking of cancer or stem cells over time [3,9], as understanding the movement, lineage and therefore differentiation of these cells as they grow into some specific tissue is vital to the understanding of these biological processes. The tracking of thousands of individual cells via reliable circle detection again provides quantitative data that would previously have been either impractical or extremely time consuming to gather manually.…”