Segmentation Of Cellular Structures Research Articles

Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations.

In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.

Segmentation of Microscopic Images of Living Cells: A Study

In this paper, three different segmentation techniques are used, namely, region growing segmentation technique, watershed segmentation and texture segmentation, to accurately extract the shapes of membranes and nuclei from the inverted microscopic images, taken throughout the monolayer formation of BGM-70 (Baby Grevit Monkey) cell culture. This strategy is a prerequisite for an accurate quantitative analysis of cell shape and morphodynamics during organogenesis and is the basis for an integrated understanding of biological processes. The segmentation of cellular structures is achieved by first normalizing the microscopic images by performing CLAHE operation followed by de-noising by morphological opening and filling of the holes and then applying segmentation algorithm for cell shape reconstruction.

Segmentation of Cells from 3-D Confocal Images of Live Embryo

Partial-differential-equation- based segmentation has been employed to accurately extract the shapes of membranes and nuclei from time lapse confocal microscopy images, taken throughout early Zebrafish embryogenesis. This strategy is a prerequisite for an accurate quantitative analysis of cell shape and morphodynamics during organogenesis and is the basis for an integrated understanding of biological processes. This data will also serve for the measurement of the variability between individuals in a population. The segmentation of cellular structures is achieved by first using an edge-preserving image filtering method for noise reduction and then applying an algorithm for cell shape reconstruction based on the Subjective Surfaces technique.