Segmentation of Overlapping Macrophages Using Anglegram Analysis

This paper describes the automatic segmentation of overlapping cells through different algorithms. As the first step, the algorithm detects junctions between the boundaries of overlapping objects based on the angles between points of the overlapping boundary. For this purpose, a novel 2D matrix with multiscale angle variation is introduced, i.e anglegram. The anglegram is used to find junctions of overlapping cells. The algorithm to retrieve junctions from the boundary was tested and validated with synthetic data and fluorescently labelled macrophages observed on embryos of Drosophila melanogaster. Then, four different segmentation techniques were evaluated: (i) a Voronoi partition based on the nuclei positions, (ii) a slicing method, which joined the clumps together (junction slicing), (iii) a partition based on the following of the edges from the junctions (edge following), and (iv) a custom self-organising map to fit to the area of overlap between the cells. Only (ii)-(iv) were based on the junctions. The segmentation results were compared based on precision, recall and Jaccard similarity. The algorithm that reported the best segmentation was the junction slicing

Segmentation and Shape Analysis of Macrophages Using Anglegram Analysis

Cell migration is crucial in many processes of development and maintenance of multicellular organisms and it can also be related to disease, e.g., Cancer metastasis, when cells migrate to organs different to where they originate. A precise analysis of the cell shapes in biological studies could lead to insights about migration. However, in some cases, the interaction and overlap of cells can complicate the detection and interpretation of their shapes. This paper describes an algorithm to segment and analyse the shape of macrophages in fluorescent microscopy image sequences, and compares the segmentation of overlapping cells through different algorithms. A novel 2D matrix with multiscale angle variation, called the anglegram, based on the angles between points of the boundary of an object, is used for this purpose. The anglegram is used to find junctions of cells and applied in two different applications: (i) segmentation of overlapping cells and for non-overlapping cells; (ii) detection of the “corners” or pointy edges in the shapes. The functionalities of the anglegram were tested and validated with synthetic data and on fluorescently labelled macrophages observed on embryos of Drosophila melanogaster. The information that can be extracted from the anglegram shows a good promise for shape determination and analysis, whether this involves overlapping or non-overlapping objects

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

Shape analysis and tracking of migrating macrophages

Cell migration is important in many human processes of development and disease. In Cancer, migration can be related to metastasis or cell defects. A precise analysis of the cell shapes in biological studies could lead to insights about migration. Therefore, this paper describes an algorithm to iteratively segment, track and analyse the shape of macrophages from fluorescent microscopy image sequences. This process allows observation of shape variations as the cells migrate. The algorithm identifies and separates overlapping and non-overlapping cells, then for the non-overlapping cases analyses the shape and extracts a series of measurements, including the number of "corner" or pointy edges through a multiscale angle variation matrix, anglegram. The shape evolution algorithm was tested on fluorescently labelled macrophages observed on embryos of Drosophila melanogaster

Analysis of the Interactions of Migrating Macrophages

Understanding the migrating patterns of cells in the immune system is of great importance; especially the changes of direction and its cause. For macrophages and other immune cells, excessive migration could be related to autoimmune diseases and cancer. In this work, an algorithm to analyse the change in direction of cells before and after they interact with another cell is proposed. The main objective is to provide insights into the notion that interactions between cell structures appear to anticipate migration. Such interactions are determined when the cells overlap and form clumps of two or more cells. The algorithm integrates a segmentation technique capable of detecting overlapping cells and a tracking framework into a tool for the analysis of the trajectories of cells before and after they overlap. The preliminary results show promise into the analysis and the hypothesis proposed, and it lays the ground work for further developments

Comparative Study of Contact Repulsion in Control and Mutant Macrophages Using a Novel Interaction Detection

In this paper, a novel method for interaction detection is presented to compare the contact dynamics of macrophages in the Drosophila embryo. The study is carried out by a framework called macrosight, which analyses the movement and interaction of migrating macrophages. The framework incorporates a segmentation and tracking algorithm into analysing the motion characteristics of cells after contact. In this particular study, the interactions between cells is characterised in the case of control embryos and Shot mutants, a candidate protein that is hypothesised to regulate contact dynamics between migrating cells. Statistical significance between control and mutant cells was found when comparing the direction of motion after contact in specific conditions. Such discoveries provide insights for future developments in combining biological experiments with computational analysis

MacroSight: A novel framework to analyze the shape and movement of interacting macrophages using MAtLABR †

This paper presents a novel software framework, called macrosight, which incorporates routines to detect, track, and analyze the shape and movement of objects, with special emphasis on macrophages. The key feature presented in macrosight consists of an algorithm to assess the changes of direction derived from cell-cell contact, where an interaction is assumed to occur. The main biological motivation is the determination of certain cell interactions influencing cell migration. Thus, the main objective of this work is to provide insights into the notion that interactions between cell structures cause a change in orientation. Macrosight analyzes the change of direction of cells before and after they come in contact with another cell. Interactions are determined when the cells overlap and form clumps of two or more cells. The framework integrates a segmentation technique capable of detecting overlapping cells and a tracking framework into a tool for the analysis of the trajectories of cells before and after they overlap. Preliminary results show promise into the analysis and the hypothesis proposed, and lays the groundwork for further developments. The extensive experimentation and data analysis show, with statistical significance, that under certain conditions, the movement changes before and after an interaction are different from movement in controlled cases

Automated Segmentation of HeLa Nuclear Envelope from Electron Microscopy Images

This paper describes an image-processing pipeline for the automatic segmentation of the nuclear envelope of HeLcells observed through Electron Microscopy. The pipeline was applied to a 3D stack of 300 images. The intermediate results of neighbouring slices are further combined to improve the final results. Comparison with a handsegmented ground truth reported Jaccard similarity values between 94-98% on the central slices with a decrease towards the edges of the cell where the structure was considerably more complex. The processing is unsupervised and each 2D slice is processed in about 5-10 seconds running on a MacBook Pro. No systematic attempt to make the code faster was made. These encouraging results could be further used to provide data for more complex segmentation techniques like Deep Learning, which require a considerable amount of data to train architectures like Convolutional Neural Networks. The code is freely available from https://github.com/reyesaldasoro/HeLa-Cell-Segmentatio