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Visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual analytics tool needs to support their required API or architecture. Contrary to most existing frameworks, we present an approach that does not rely on a single predefined interchange mechanism for the entire ensemble of VA tools. Instead, we propose using any available channel for data exchange between two consecutive VA tools. This allows mixing and matching of different data exchange strategies over the course of a cross-tool analysis. In this paper, we identify the challenges associated with establishing such tool chaining platform for data-driven coordination. We further describe the structure and capabilities of data exchange and explain various functionalities of our platform in detail. Based on a demonstrating example, we discuss the limitations of our approach and elaborate new insight for the coordination of the visual output of multiple VA tools.

A common severe neurotoxic side effect of breast cancer (BC) therapy is chemotherapy-induced peripheral neuropathy (CIPN) and intervention is highly needed for the detection, prevention, and treatment of CIPN at an early stage. As the eye is susceptible to neurotoxic stimuli, the present study aims to determine whether CIPN signs in paclitaxel-treated BC patients correlate with ocular changes by applying advanced non-invasive biophotonic in vivo imaging. Patients (n = 14, 10 controls) underwent monitoring sessions after diagnosis, during, and after therapy (T0-T3). Monitoring sessions included general anamnesis, assessment of their quality of life, neurological scores, ophthalmological status, macular optical coherence tomography (OCT), and imaging of their subbasal nerve plexus (SNP) by large-area confocal laser-scanning microscopy (CLSM). At T0, no significant differences were detected between patients and controls. During treatment, patients’ scores significantly changed while the greatest differences were found between T0 and T3. None of the patients developed severe CIPN but retinal thickenings could be detected. CLSM revealed large SNP mosaics with identical areas while corneal nerves remained stable. The study represents the first longitudinal study combining oncological examinations with advanced biophotonic imaging techniques, demonstrating a powerful tool for the objective assessment of the severity of neurotoxic events with ocular structures acting as potential biomarkers.

Optical coherence tomography (OCT) supports the detection of thickness changes in intraretinal layers at an early stage of diabetes mellitus. However, the analysis of OCT data in cross-sectional studies is complex and time-consuming. We introduce an enhanced deviation map-based analysis (MA) and demonstrate its effectiveness in detecting early changes in intraretinal layer thickness in adults with type 2 diabetes mellitus (T2DM) compared to common early treatment diabetic retinopathy study (ETDRS) grid-based analysis (GA). To this end, we obtained OCT scans of unilateral eyes from 33 T2DM patients without diabetic retinopathy and 40 healthy controls. The patients were categorized according to concomitant diabetic peripheral neuropathy (DN). The results of MA and GA demonstrated statistically significant differences in retinal thickness between patients and controls. Thinning was most pronounced in total retinal thickness and the thickness of the inner retinal layers in areas of the inner macular ring, selectively extending into areas of the outer macular ring and foveal center. Patients with clinically proven DN showed the strongest thinning of the inner retinal layers. MA showed additional areas of thinning whereas GA tended to underestimate thickness changes, especially in areas with localized thinning. We conclude that MA enables a precise analysis of retinal thickness data and contributes to the understanding of localized changes in intraretinal layers in adults with T2DM.

The ongoing proliferation and differentiation of Visual Analytics to various application domains and usage scenarios has also resulted in a fragmentation of the software landscape for data analysis. Highly specialized tools are available that focus on one particular analysis task in one particular application domain. The interoperability of these tools, which are often research prototypes without support or proper documentation, is hardly ever considered outside of the toolset they were originally intended to work with. To nevertheless use and reuse them in other settings and together with other tools, so as to realize novel analysis procedures by using them in concert, we propose an approach for loosely coupling individual visual analytics tools together into toolchains. Our approach differs from existing such mechanisms by being lightweight in realizing a pairwise coupling between tools without a central broker, and by being layered into different aspects of such a coupling: the usage flow, the data flow, and the control flow. We present a model of this approach and showcase its usefulness with three different usage examples, each focusing on one of the layers.

Optical coherence tomography (OCT) enables noninvasive high-resolution 3D imaging of the human retina, and thus plays a fundamental role in detecting a wide range of ocular diseases. Despite the diagnostic value of OCT, managing and analyzing resulting data is challenging. We apply two visual analysis strategies for supporting retinal assessment in practice. First, we provide an interface for unifying and structuring data from different sources into a common basis. Fusing that basis with medical records and augmenting it with analytically derived information facilitates thorough investigations. Second, we present a tailored visual analysis tool for presenting, emphasizing, selecting, and comparing different aspects of the attributed data. This enables free exploration, reducing the data to relevant subsets, and focusing on details. By applying both strategies, we effectively enhance the management and the analysis of retinal OCT data for assisting medical diagnoses. Domain experts applied our solution successfully to study early retinal changes in patients suffering from type 1 diabetes mellitus.

ZusammenfassungDie optische Kohärenztomografie (OCT) als bildgebendes In-vivo-Verfahren eröffnet völlig neue Möglichkeiten bei der Diagnose von Netzhaut- und Sehnervenerkrankungen. Im Rahmen einer Patientenuntersuchung entstehen hochaufgelöste Volumendatensätze, die kleinste Veränderungen der Netzhaut, wie sie in einem sehr frühen Krankheitsstadium auftreten, erfassen können. Allerdings erschwert die Komplexität des Datenmaterials auch die Auswertung. Deshalb werden die Daten üblicherweise zunächst auf ein handhabbares Maß reduziert, wobei meist automatische, stark vereinfachende Methoden eingesetzt werden. Dies macht es schwer, wenn nicht sogar unmöglich, minimale strukturelle, gegebenenfalls auch lokale Veränderungen der Netzhaut zu extrahieren, um eine entsprechende Diagnose zu stellen. Unser Ziel ist es, die bisherigen etablierten Verfahren um visuell-interaktive Analysemethoden zu ergänzen. Damit soll sowohl eine patientenspezifische Selektion relevanter Daten als auch die Erkennung minimaler Netzhautveränderungen unterstützt werden. Je nach Fragestellung visualisieren wir die OCT-Daten aus unterschiedlichen Perspektiven. Anhand dieser Bilder kann gezielt eine individualisierte Datenselektion durchgeführt werden. Die relevanten Datenbereiche lassen sich dann im Kontext der gesamten Datenmenge hervorheben oder in separaten sogenannten Views anzeigen. So wird es möglich, relevante Substrukturen im Detail zu analysieren und mit Vergleichsdaten zu korrelieren. Insbesondere für Kohortenvergleiche und Vergleiche zwischen Individuen und einer Kontrollgruppe ergeben sich dadurch neue klinische und experimentelle Möglichkeiten. Wir belegen dies beispielhaft anhand einer Studie mit pädiatrischen Patienten mit Diabetes mellitus Typ 1 (T1DM). Unsere Ergebnisse zeigen, dass durch die Einbindung von visuell-interaktiven Analysemethoden entscheidende Fortschritte hinsichtlich einer patientenspezifischen Diagnostik sowie zur Beurteilung von klinischen Studien möglich werden.

In 3D visualization of terrain, occlusion has detrimental impact on discovering, accessing, and spatially relating embedded data. This can lead to significant loss of information. To address this issue, we introduce visibility widgets: compact auxiliary views that visualize occluded data, their locations, and related properties, and thus support users in revealing hidden information. The widgets comprise different occlusion management strategies for detecting and analyzing invisible data inside as well as outside of the field of view. In addition, each widget is designed complementary and transient to facilitate interactive exploration and navigation while not monopolizing the user’s attention. We demonstrate the application of visibility widgets focusing the visual exploration of weather data in 3D terrain.

Clinical data have their own peculiarities, as they evolve over time, may be incomplete, and are highly heterogeneous. These characteristics turn a thorough analysis into a challenging task, especially since domain experts are aware of the data flaws, which may impact their trust in the data. As we obtained anonymized clinical data from more than 3,500 patients with retinal diseases, we have to address these challenges. We define a workflow that integrates data cleansing and exploration in an iterative process, so that users are able to easily find anomalies and patterns in the data at any point in their analysis. We implement our workflow in a user-centered visual analytics tool with dedicated visualization and interaction techniques. In collaboration with experts, we apply our tool to examine the interdependency between patients’ visual acuity developments and treatment patterns. We find, that real-life data often have unforeseen incidents which can strongly influence the overall visual acuity development. This differs to study results, which are usually conducted under restrictive conditions and have shown visual acuity improvement with on-schedule treatment.

Optical coherence tomography (OCT) enables high-resolution 3D imaging of intraretinal layers. The layers' thickness are commonly compared to normative data to understand a variety of retinal and systemic disorders. Yet, discovering and measuring localized thickness differences in the complex OCT data is challenging, particularly in an early stage of eye diseases. We combine interactive visualization and automated measurements to support the analysis of abnormal layer thickness. First, the spatial distribution of thickness differences is visualized via precise deviation maps. Second, statistical tests are applied to quantify every point of the visualized data. Third, measurements of significant differences for anatomically distinct areas and selected regions of interest are retrieved. We demonstrate the effectiveness of our solution in detecting abnormal layer thickness in the context of a cross-sectional ophthalmic study.

Purpose: To analyze the use of deviation maps (DevMs) to understand thickness changes of inner retinal layers in optical coherence tomography (OCT) data. To test a new visual analytics (VA) method with reduced complexity of OCT data analysis by comparing the layer thickness of children with type 1 diabetes mellitus (T1DM) to matched controls. Methods: OCT was performed on unilateral eyes of 26 children with T1DM without diabetic retinopathy and 29 healthy children to obtain macular volume scans. Subsequently, segmented inner retinal layers were analyzed using VA. Deviation maps were generated to readily visualize thickness differences between both groups and to investigate thickness changes of individual patients in relation to the control group. Results: In DevMs of the patient group, the total retina (TR) demonstrated localized, irregular areas of thinning (mean ± standard deviation) involving foveal center, inner macula, and inferior-nasal outer macula (−9.31 ± 1.73 µm; p < 0.05). Similarly, retinal nerve fiber layer showed continuous and localized areas of thinning in both inner and outer macula, extending nasally (−5.45 ± 4.31 µm; p < 0.05). In DevMs of individual patients, the TR and inner retinal layers revealed remarkable changes in thickness that were present between patients at both late and early stages of diabetes. Conclusion: The VA method simplifies the in-depth analysis of OCT volume data from different groups and is effective in detecting retinal thickness changes in children with diabetes. It can be easily adopted in a clinical set-up and intuitively used in complex multidisciplinary studies.

Objective: Children with chronic kidney diseases (CKD) are at risk for neurological diseases at early adulthood. Spectral-domain optical coherence tomography (SD-OCT) of the retina is especially suitable for determination of intraretinal layer thickness. We wonder whether retinal thinning is already present in pediatric patients with mild-to-moderate CKD. Patients and Methods: Children (n = 15; 14.9 ± 2.4 years) with mild-to-moderate CKD (median eGFR of 95ml/min/1.73m2; range: 28–187ml/min/1.73m2) due to glomerulopathy, congenital anomalies of kidney and urinary tract (CAKUT), or haemolytic uremic syndrome (HUS) underwent a detailed ophthalmologic examination including high-resolution SD-OCT. Three OCT scans were obtained from the right eyes of all patients. Within each scan, retinal layers were separated and the mean thickness was determined at the foveal, parafoveal, and perifoveal area. The results were compared to those we obtained previously from healthy children. Results: At the parafoveal area, thickness (median, range) of the total retina (ALL), ganglion cell layer (GCL), and inner plexiform layer (IPL) were reduced compared to healthy volunteers (339µm, (288–361µm) vs. 348µm, (320–385µm); 49.8µm (30.5–56.6µm) vs. 53.5μm (49.5–60.5µm) and 41.0µm (29.4–43.7µm) vs. 43.46µm (39.5–46.3μm); each p Conclusion: Distinct changes within the parafoveal area of the total retina, GCL, and IPL are present in children with mild-to-moderate CKD. Prospective studies are required to assess the clinical significance of our findings.

Optical coherence tomography (OCT) of the retina and corneal confocal laser scanning microscopy (CLSM) of the subbasal nerve plexus (SBP) are noninvasive techniques for quantification of the ocular neurodegenerative changes in individuals with type 1 diabetes mellitus (T1DM). In adult T1DM patients these changes are hardly related to T1DM only. Instead, ageing and/or lifestyle associated comorbidities have to be considered as putative confounding variables. Therefore, we investigated pediatric T1DM patients (n = 28; 14.2 ± 2.51 y; duration of disease: 5.39 ± 4.16 y) without clinical signs of diabetic retina disease, neuropathy, vasculopathy or nephropathy and compared our findings with those obtained in healthy controls (n = 46; 14.8 ± 1.89 y). The SBP was characterized by the averaged length, thickness, and tortuosity of nerve fibers as well as the number of branching and connecting points. OCT was used to determine the total thickness of the retina (ALL) and the thickness of each retinal layer. Both methods revealed signs of early neurodegenerative changes, e.g. thinning of distinct retinal layers at the pericentral ring and shortening of corneal nerve fibers that are already present in pediatric T1DM patients. Standardization of instruments and algorithms are urgently required to enable uniform comparison between different groups and define normative values to introduce in the clinical setting.

Die diabetische Neuropathie (DN) gehort zu den haufigsten Folgeerkrankungen des Diabetes mellitus. Dabei treten Schadigungen des sensiblen Nervensystems auf, die sich auch im subbasalen Nervenplexus der Kornea manifestieren. Mittels der konfokalen Laser-Scanning-Mikroskopie sind diese einer In-vivo-Visualisierung zuganglich. Dieser Beitrag untersucht Marker, die Alterationen dieser kornealen Nervenfasern beschreiben. Mittels statistischer Berechnungen und einer interaktiv-visuellen Analyse der Daten aus 2 klinischen Studien werden charakteristische Merkmale und Auspragungen extrahiert, mit dem Ziel, DN-Patienten und gesunde Probanden zu trennen. Durch diese Klassifikation soll die Diagnose der diabetischen Neuropathie unterstutzt werden. Ausgangspunkt fur die visuelle Analyse sind verschiedene Darstellungen, die sowohl die Ergebnisse der statistischen Berechnungen als auch die erfassten Daten im multidimensionalen Zusammenhang zeigen. Auf alle Darstellungen kann der Nutzer interaktiv zugreifen, um sie gesondert anzuzeigen und auszuwerten. Durch diese visuelle Betrachtung soll die Klassifikation der Probanden genauer analysiert werden. Unklare Zuordnungen werden dabei einer separaten Klasse, der so bezeichneten „fuzzy group“ zugewiesen. Fur diese Probanden ist eine weiterfuhrende Diagnostik notwendig.

Optical coherence tomography (OCT) enables noninvasive high-resolution 3D imaging of the human retina, and thus plays a fundamental role in ophthalmology. Via OCT examination, even subtle retinal changes can be captured, which occur in very early stages of different diseases (e.g., glaucoma, diabetes mellitus, or age-related macular degeneration). Yet, analyzing the resulting data is challenging. Conventionally, OCT data are strongly aggregated via automated methods. While this reduces the amount of information to be analyzed, it also makes it difficult, if not impossible, to identify small and localized retinal changes. This might lead to wrong diagnoses, since these methods do not account for patient-specific characteristics. We address this problem by providing new and efficient visual-interactive methods. Particularly, we introduce dedicated visualizations that show different aspects of the data. In addition, we support patient-specific selections of relevant data regions. Selected regions are emphasized, or separately visualized to inspect retinal substructures in detail. By visually comparing the regions to reference data, even very small retinal changes can be detected. We demonstrate the utility of our approach by applying it to data of a study with pediatric patients suffering from diabetes mellitus type 1. Our results show that visual-interactive methods indeed help to analyze subtle retinal changes and, thus, support the diagnosis of diseases in an early stage.Die optische Kohärenztomografie (OCT) als bildgebendes In-vivo-Verfahren eröffnet völlig neue Möglichkeiten bei der Diagnose von Netzhaut- und Sehnervenerkrankungen. Im Rahmen einer Patientenuntersuchung entstehen hochaufgelöste Volumendatensätze, die kleinste Veränderungen der Netzhaut, wie sie in einem sehr frühen Krankheitsstadium auftreten, erfassen können. Allerdings erschwert die Komplexität des Datenmaterials auch die Auswertung. Deshalb werden die Daten üblicherweise zunächst auf ein handhabbares Maß reduziert, wobei meist automatische, stark vereinfachende Methoden eingesetzt werden. Dies macht es schwer, wenn nicht sogar unmöglich, minimale strukturelle, gegebenenfalls auch lokale Veränderungen der Netzhaut zu extrahieren, um eine entsprechende Diagnose zu stellen. Unser Ziel ist es, die bisherigen etablierten Verfahren um visuell-interaktive Analysemethoden zu ergänzen. Damit soll sowohl eine patientenspezifische Selektion relevanter Daten als auch die Erkennung minimaler Netzhautveränderungen unterstützt werden. Je nach Fragestellung visualisieren wir die OCT-Daten aus unterschiedlichen Perspektiven. Anhand dieser Bilder kann gezielt eine individualisierte Datenselektion durchgeführt werden. Die relevanten Datenbereiche lassen sich dann im Kontext der gesamten Datenmenge hervorheben oder in separaten sogenannten Views anzeigen. So wird es möglich, relevante Substrukturen im Detail zu analysieren und mit Vergleichsdaten zu korrelieren. Insbesondere für Kohortenvergleiche und Vergleiche zwischen Individuen und einer Kontrollgruppe ergeben sich dadurch neue klinische und experimentelle Möglichkeiten. Wir belegen dies beispielhaft anhand einer Studie mit pädiatrischen Patienten mit Diabetes mellitus Typ 1 (T1DM). Unsere Ergebnisse zeigen, dass durch die Einbindung von visuell-interaktiven Analysemethoden entscheidende Fortschritte hinsichtlich einer patientenspezifischen Diagnostik sowie zur Beurteilung von klinischen Studien möglich werden.

Visualizing geo-spatial data embedded into a three-dimensional terrain is challenging. The problem becomes even more complex when uncertainty information needs to be presented as well. This paper addresses the question of how to visually communicate all three aspects: the 3D terrain, the geo-spatial data, and the data-associated uncertainty. We argue that visualizing all aspects with a high degree of detail will likely exceed the visual budget. Therefore, we propose a visualization strategy based on prioritizing a selected aspect and presenting the remaining two with less detail. We discuss various design options that allow us to obtain differently prioritized visual representations. Our approach has been implemented as a tool for rapid visualization prototyping in the context of avionics applications. Practical solutions are described for a use case related to the visualization of 3D terrain and uncertain weather data.

In 3D visualization of terrain, occlusion has detrimental impact on discovering, accessing, and spatially relating embedded data. This can lead to significant loss of information. To address this issue, we present visibility widgets: compact auxiliary views that visualize occluded data, their locations, and related properties, and thus, support users in revealing hidden information. Our widgets comprise different occlusion management strategies for detecting and analyzing invisible data inside as well as outside of the field of view. In addition, each widget is designed complementary and transient to facilitate interactive exploration and navigation while not monopolizing the user's attention. We demonstrate the application of our visibility widgets in a flexible visualization tool, focusing on visual exploration of hazardous weather phenomena in 3D terrain.

Recognizing activities has become increasingly relevant in many application domains, such as security or ambient assisted living. To handle different scenarios, the underlying automated algorithms are configured using multiple input parameters. However, the influence and interplay of these parameters is often not clear, making exhaustive evaluations necessary. On this account, we propose a visual analytics approach to supporting users in understanding the complex relationships among parameters, recognized activities, and associated accuracies. First, representative parameter settings are determined. Then, the respective output is computed and statistically analyzed to assess parameters' influence in general. Finally, visualizing the parameter settings along with the activities provides overview and allows to investigate the computed results in detail. Coordinated interaction helps to explore dependencies, compare different settings, and examine individual activities. By integrating automated, visual, and interactive means users can select parameter values that meet desired quality criteria. We demonstrate the application of our solution in a use case with realistic complexity, involving a study of human protagonists in daily living with respect to hundreds of parameter settings.

Exploring and comparing categorical time series and finding temporal patterns are complex tasks in the field of time series data mining. Although different analysis approaches exist, these tasks remain challenging, especially when numerous time series are considered at once. We propose a visual analysis approach that supports exploring such data by ordering time series in meaningful ways. We provide interaction techniques to steer the automated arrangement and to allow users to investigate patterns in detail.

Reconstructing processes from measurements of multiple sensors over time is an important task in many application domains. For the reconstruction, these multivariate time-series can be automatically processed. However, the outcomes of automated algorithms often vary in quality and show strong parameter dependencies, making manual inspections and adjustments of the results necessary. We propose a visual analysis approach to support the user in understanding parameters' influences on these results. With our approach the user can identify and select parameter settings that meet certain quality criteria. The proposed visual and interactive design helps to identify relationships and temporal patterns, supports subsequent decision making, and promotes higher accuracy as well as confidence in the results.

The question whether to use 2D or 3D for data visualization is generally difficult to decide. Two-dimensional and three-dimensional visualization techniques exhibit different advantages and disadvantages related to various perceptual and technical aspects such as occlusion, clutter, distortion, or scalability. To facilitate problem understanding and comparison of existing visualization techniques with regard to these aspects, this report introduces a systematization based on presentation characteristics. It enables a categorization with respect to combinations of static 2D and 3D presentations of attributes and their spatial reference. Further, it complements existing systematizations of data in an effort to formalize a common terminology and theoretical framework for this problem domain. We demonstrate our approach by reviewing different visualization techniques of spatial data according to the presented systematization.

With the advancement of Visual Analytics (VA) and its spread into various application fields comes along a specialization of methods and tools. This adds complexity and requires extra effort when devising domain-dependent VA solutions, as for every new domain question a new specialized tool or framework must be developed. In this paper, we investigate the possibility of using and re-using existing tools – domain-dependent and general-purpose – by loosely coupling them into specialized VA tool ensembles as needed. We call such coupling among independent tools lightweight coordination, as it is minimally-invasive, pair-wise, and opportunistic in utilizing whichever interface a VA tool offers. We propose the use of lightweight coordination for managing the workflow, the data flow, and the control flow among VA tools, and we show how it can be supported with suitable setups of the multiple tool UIs involved. This concept of lightweight coordination is exemplified with a health care scenario, where an ensemble of independent VA tools is used in a concerted way to pursue the visual analysis of a patient’s troublesome vital data.