Abstract Live cell imaging is an important biomedical research paradigm for studying dynamic cellular behaviour. Although phenotypic data derived from images are difficult to explore and analyse, some researchers have successfully addressed this with visualization. Nonetheless, visualization methods for live cell imaging data have been reported in an ad hoc and fragmented fashion. This leads to a knowledge gap where it is difficult for biologists and visualization developers to evaluate the advantages and disadvantages of different visualization methods, and for visualization researchers to gain an overview of existing work to identify research priorities. To address this gap, we survey existing visualization methods for live cell imaging from a visualization research perspective for the first time. Based on recent visualization theory, we perform a structured qualitative analysis of visualization methods that includes characterizing the domain and data, abstracting tasks, and describing visual encoding and interaction design. Based on our survey, we identify and discuss research gaps that future work should address: the broad analytical context of live cell imaging; the importance of behavioural comparisons; links with dynamic data visualization; the consequences of different data modalities; shortcomings in interactive support; and, in addition to analysis, the value of the presentation of phenotypic data and insights to other stakeholders.

BACKGROUND: Biomedical image processing methods require users to optimise input parameters to ensure high-quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. RESULTS: We present a visualisation method that transforms users' ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. CONCLUSIONS: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches.

Abstract Cell lineages describe the developmental history of Cell populations and are produced by combining time‐lapse imaging and image processing. Biomedical researchers study Cell lineages to understand fundamental processes such as Cell differentiation and the pharmacodynamic action of anticancer agents. Yet, the interpretation of Cell lineages is hindered by their complexity and insufficient capacity for visual analysis. We present a novel approach for interactive visualisation of Cell lineages. Based on an understanding of Cellular biology and live‐Cell imaging methodology, we identify three requirements: multimodality (Cell lineages combine spatial, temporal, and other properties), symmetry (related to lineage branching structure), and synchrony (related to temporal alignment of Cellular events). We address these by combining visual summaries of the spatiotemporal behaviour of an arbitrary number of lineages, including variation from average behaviour, with node‐link representations that emphasise the presence or absence of symmetry and synchrony. We illustrate the merit of our approach by presenting a real‐world case study where the cytotoxic action of the anticancer drug topotecan was determined.

Image analysis algorithms are often highly parameterized and much human input is needed to optimize parameter settings. This incurs a time cost of up to several days. We analyze and characterize the conventional parameter optimization process for image analysis and formulate user requirements. With this as input, we propose a change in paradigm by optimizing parameters based on parameter sampling and interactive visual exploration. To save time and reduce memory load, users are only involved in the first step--initialization of sampling--and the last step--visual analysis of output. This helps users to more thoroughly explore the parameter space and produce higher quality results. We describe a custom sampling plug-in we developed for CellProfiler--a popular biomedical image analysis framework. Our main focus is the development of an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. We implemented this in a prototype called Paramorama. It provides users with a visual overview of parameters and their sampled values. User-defined areas of interest are presented in a structured way that includes image-based output and a novel layout algorithm. To find optimal parameter settings, users can tag high- and low-quality results to refine their search. We include two case studies to illustrate the utility of this approach.

We present a new method for the visual analysis of multivariate system traces. Our method combines three perspectives: (1) a schematic diagram, (2) time series plots and (3) a state transition graph. After we show how these perspectives are related, we discuss their integration into a single solution for the visual analysis of multivariate system traces. The combination of the three perspectives provides the user with a rich analysis interface that enables gaining significant insight into system behavior. We illustrate some of the advantages of our approach by providing a real-world use case.

Abstract Most graph visualization techniques focus on the structure of graphs and do not offer support for dealing with node attributes and edge labels. To enable users to detect relations and patterns in terms of data associated with nodes and edges, we present a technique where this data plays a more central role. Nodes and edges are clustered based on associated data. Via direct manipulation users can interactively inspect and query the graph. Questions that can be answered include, “which edge types are activated by specific node attributes?” and, “how and from where can I reach specific types of nodes?” To validate our approach we contrast it with current practice. We also provide several examples where our method was used to study transition graphs that model real‐world systems.

We present a new approach for the visual analysis of state transition graphs. We deal with multivariate graphs where a number of attributes are associated with every node. Our method provides an interactive attribute-based clustering facility. Clustering results in metric, hierarchical and relational data, represented in a single visualization. To visualize hierarchically structured quantitative data, we introduce a novel technique: the bar tree. We combine this with a node-link diagram to visualize the hierarchy and an arc diagram to visualize relational data. Our method enables the user to gain significant insight into large state transition graphs containing tens of thousands of nodes. We illustrate the effectiveness of our approach by applying it to a real-world use case. The graph we consider models the behavior of an industrial wafer stepper and contains 55 043 nodes and 289 443 edges.

Transition systems are graphs and it is natural to consider their visual representation as a graph visualization problem. They also pose an interesting multidimensional visualization challenge since every state may be considered as a point in n-space. We discuss a number of approaches toward projecting transition systems to the 2D plane by considering the dimensionality of the states. To assess these techniques, we consider their ability to assist us in answering questions that are difficult to address with conventional methods.

Ontology bases are sets of binary fact types called lexons. This paper discusses LexoVis, a lexon visualization tool that addresses the inherent size and scale of ontology bases. LexoVis facilitates the analysis of lexons by taking a well-ordered approach and provides overview and detail by employing the graphical fisheye view. Furthermore, different ordering and clustering heuristics incorporated in LexoVis offer insights into ontology bases not explicit in text-based representations.