Shape Analysis and Tracking of Migrating Macrophages

This work describes an algorithm to observe cell shape variation associated with migration. The algorithm iteratively segments, tracks and analyses the shape of macrophages in Drosophila melanogaster embryos. Analysis of shape, including the number of corners or pointy edges, rely on a novel approach to finding junctions, the anglegram matrix. The anglegram [1] IS a multiscale angle variation 2D matrix. It Iis constructed by calculating inner point angles alongside the boundaries of an object

Algorithm to Compute Reduced Costs on a Graph

The problem of calculating the Reduced Costs of all arcs on a graph is considered. For each arc on the graph, the problem is to determine the arc with maximum cost on the fundamental path on the corresponding spanning tree. A new algorithm for this problem is proposed. It is based on the construction of a Binary Tree by sequential deletion of arcs in a descending order of costs. The tree is composed of leaf nodes representing the actual vertices in the graph and intermediate nodes representing the branches of the Minimum Spanning Tree. Using the Binary Tree, the Reduced Costs of any chord is determined by the Nearest Common Ancestor of the leaf nodes corresponding to the chord vertices. Computational results are presented for graphs of various densities. The algorithm's performance is compared to the path labelling algorithm of Carpaneto

An optimal control approach to cell tracking

Cell tracking is of vital importance in many biological studies, hence robust cell tracking algorithms are needed for inference of dynamic features from (static) in vivo and in vitro experimental imaging data of cells migrating. In recent years much attention has been focused on the modelling of cell motility from physical principles and the development of state-of-the art numerical methods for the simulation of the model equations. Despite this, the vast majority of cell tracking algorithms proposed to date focus solely on the imaging data itself and do not attempt to incorporate any physical knowledge on cell migration into the tracking procedure. In this study, we present a mathematical approach for cell tracking, in which we formulate the cell tracking problem as an inverse problem for fitting a mathematical model for cell motility to experimental imaging data. The novelty of this approach is that the physics underlying the model for cell migration is encoded in the tracking algorithm. To illustrate this we focus on an example of Zebrafish (Danio rerio's larvae) Neutrophil migration and contrast an ad-hoc approach to cell tracking based on interpolation with the model fitting approach we propose in this study

Quantification of the Effects of Low Dose Radiation and its Impact on Cardiovascular Risks

This work describes an algorithm developed to quantify the effects of low dose radiation on the cardiac endothelial cells with the final objective of inferring how radiation may potentially initiate cardiovascular disease in post radiotherapy-treated patients. The effects are investigated by using an in-vitro co-culture cellular matrix, consisting of endothelial cells on a base of fibroblasts, which in time begin to form capillary (tubular) like structures. A range of radiation doses (0.2-16 Gray (Gy)) was applied to different samples and the effects observed. The automatic segmentation is validated against a set of manual segmented images with satisfactory results presenting a correct classification of 0.93; classification is the measure of comparison between two sets of images, specified as a number from 0 to 1, whereby 1 denotes 100% similarity whilst 0 refers to 0% similarity. Measurements related to geometrical parameters were further obtained. It was found during the course of this project, the largest observable change in endothelial cell structure was found after exposure to 0.2 Grays of radiation

Automatic segmentation of centromeres, foci and delineation of chromosomes

The observation of chromosomes has been crucial for our understanding of their structure, function, organization, and evolution of genes and genomes as well as morphological changes during mitotic and meiotic divisions. In this work, we present an automatic algorithm for the segmentation of centromeres and foci of DNA processing proteins, as well as the delineation of convoluted chromosomes. The algorithm is fully automatic and does not require tuning of parameters. Statistical measurements of numbers, areas distance and lengths are provided by the algorithm. The work is preliminary as this algorithm has not been tested on a large database nor used to differentiate between populations, however, it is considered that given it is fully automatic and fast it should be a useful tool for the analysis of chromosomes

Analysis of capillary-like structures formed by endothelial cells in a novel organotypic assay developed from heart tissue

In this work, we present an algorithm to perform automatic segmentation, feature extraction and dosage classification of images derived from a novel organotypic angiogenesis assay developed to assess the effects of ionising radiation in the mouse heart. The images presented very different conditions of illumination and conditions of density and shapes of cells. The algorithm consists of a pipeline of several steps, which were validated against hand-segmented images. The algorithm provided satisfactory results, as all images were correctly dose-classified. The cells exposed to the lowest radiation dose were observed to have the greatest relative feature variability

Automatic segmentation of focal adhesions from mouse embryonic fibroblasts

This work describes an automatic algorithm for the segmentation and quantification of focal adhesions from mouse embryonic fibroblasts. The main challenges solved by this algorithm are: the variability of the intensity of the focal adhesions, the detection of an outer ring, which distinguishes the cell periphery responsible for the cell migration, and the quantification of the characteristics of the focal adhesions. The algorithm detects maximal regions through gradients and uses a region-growing algorithm limited by intensity-based edges. The outer ring is calculated based on the average radial intensity from an extended centroid of the cell. Finally, traditional morphological characteristics are obtained to distinguish between two groups of cells. Two of the measurements employed showed statistical difference between two groups of cells