Analysis of live cell images: methods, tools and opportunities

Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug development. Image analysis methods are needed to extract quantitative information from these vast and complex data sets. The aim of this review is to provide an overview of available image analysis methods for live cell imaging, in particular required preprocessing image segmentation, cell tracking and data visualisation methods. The potential opportunities recent advances in machine learning, especially deep learning, and computer vision provide are being discussed. This review includes overview of the different available software packages and toolkits

Analysis of live cell images: methods, tools and opportunities

Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug development. Image analysis methods are needed to extract quantitative information from these vast and complex data sets. The aim of this review is to provide an overview of available image analysis methods for live cell imaging, in particular required preprocessing image segmentation, cell tracking and data visualisation methods. The potential opportunities recent advances in machine learning, especially deep learning, and computer vision provide are being discussed. This review includes overview of the different available software packages and toolkits

Dynamic classification of defect structures in molecular dynamics simulation data

In this application paper we explore techniques to classify anomalous structures (defects) in data generated from abinitio Molecular Dynamics (MD) simulations of Silicon (Si) atom systems. These systems are studied to understand the processes behind the formation of various defects as they have a profound impact on the electrical and mechanical properties of Silicon. In our prior work we presented techniques for defect detection [11, 12, 14]. Here, we present a two-step dynamic classifier to classify the defects. The first step uses up to third-order shape moments to provide a smaller set of candidate defect classes. The second step assigns the correct class to the defect structure by considering the actual spatial positions of the individual atoms. The dynamic classifier is robust and scalable in the size of the atom systems. Each phase is immune to noise, which is characterized after a study of the simulation data. We also validate the proposed solutions by using a physical model and properties of lattices. We demonstrate the efficacy and correctness of our approach on several large datasets. Our approach is able to recognize previously seen defects and also identify new defects in real time.

Volume Deformation via Scattered Data Interpolation

With the advent of contemporary GPUs, it has been possible to perform volume deformation at interactive rates. In particular, it has been shown that deformation can be important for the purposes of illustration. In such cases, rather than being the result of a physically-based simulation, volume deformation is often goal-oriented and user-guided. For this purpose, it is important to provide the user with tools for directly specifying a deformation interactively and refine it based on constraints or user intention. In many cases, deformation is obtained based on a reference object or image. In this paper, we present a method for deforming volumetric objects based on user guided scattered data interpolation. A GPU-based implementation enables real-time manipulation of 2D images and volumes. We show how this approach can have applications in scientific illustration, volume exploration and visualization, generation of animations and special effects, among others. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Line and Curve Generation 1