We present a systematic approach for training and testing structural texture similarity metrics (STSIMs) so that they can be used to exploit texture redundancy for structurally lossless image compression. The training and testing is based on a set of image distortions that reflect the characteristics of the perturbations present in natural texture images. We conduct empirical studies to determine the perceived similarity scale across all pairs of original and distorted textures. We then introduce a data-driven approach for training the Mahalanobis formulation of STSIM based on the resulting annotated texture pairs. Experimental results demonstrate that training results in significant improvements in metric performance. We also show that the performance of the trained STSIM metrics is competitive with state of the art metrics based on convolutional neural networks, at substantially lower computational cost.

@en

We investigated the influence of the medium on the perception of depicted objects and materials. Oil paintings and their reproductions in engravings were chosen because they are vastly distinctive media while having completely identical content. A total of 15 pairs were collected, consisting of 88 fragments depicting different materials, including fabric, skin, wood and metal. Besides the original condition, we created three manipulations to understand the effect of colour (a greyscale version) and contrast (equalised histograms towards both painting and engraving). We performed rating experiments on five attributes: three-dimensionality, glossiness, convincingness, smoothness and softness. An average of 25 participants finished each of the 20 online experimental sessions (five attributes X four conditions). Besides clear correlations between the two media, the differences mainly show in their means (different levels of perceived attributes) and standard deviations (perceived range). In most sessions, paintings depict a wider range than engravings. In addition, it was the histogram equalisation (global contrast) that made the most impact on perceived attributes, rather than colour removal. This suggests that engravers compensated for the lack of colour by exploiting the possibilities of local contrast.

@en

Artificial Intelligence (AI)-based auto-delineation technologies rapidly delineate multiple structures of interest like organs-at-risk and tumors in 3D medical images, reducing personnel load and facilitating time-critical therapies. Despite its accuracy, the AI may produce flawed delineations, requiring clinician attention. Quality assessment (QA) of these delineations is laborious and demanding. Delineation error detection systems (DEDS) aim to aid QA, yet questions linger about potential challenges to their adoption and time-saving potential. To address these queries, we first conducted a user study with two clinicians from Holland Proton Therapy Center, a Dutch cancer treatment center. Based on the study’s findings about the clinicians’ error detection workflows with and without DEDS assistance, we developed a simulation model of the QA process, which we used to assess different error detection workflows on a retrospective cohort of 42 head and neck cancer patients. Results suggest possible time savings, provided the per-slice analysis time stays close to the current baseline and trading-off delineation quality is acceptable. Our findings encourage the development of user-centric delineation error detection systems and provide a new way to model and evaluate these systems’ potential clinical value.@en

Most studies on the perception of style have used whole scenes/entire paintings; in our study, we isolated a single motif (an apple) to reduce or even eliminate the influence of composition, iconography, and other contextual information. In this article, we empirically address two fundamental questions of the existence (Experiment 1) and description (Experiment 2) of style. We chose 48 cut-outs of mostly Western European paintings (15th to 21st century) that showed apples. In Experiment 1, 415 unique participants completed online triplet similarity tasks. Multidimensional scaling (MDS) reached a nonrandom three-dimensional (3D) embedding, showing that participants are able to judge stylistic differences in a systematic way. We also found a strong correlation between creation year and embedding, both a linear correlation with Dimension 2, and a rotational correlation in the first two dimensions. To interpret the embedding further, in Experiment 2, we fitted three color statistics and nine attribute ratings (glossiness, three-dimensionality, convincingness, brush coarseness, etc.) to the 3D perceptual style space. Results showed that Dimension 1 is associated with spatial attributes (Smoothness, Brushstroke coarseness) and Convincingness, Dimension 2 is related to Hue, and Dimension 3 is related to Chroma. The results suggest that texture and color are two important variables for style perception. By isolating the motifs, we could exclude higher levels of information such as composition and context. Interestingly, the results reinforce previous findings using whole scenes, suggesting that style can already be perceived in sometimes very small fragments of paintings.

@en

Artificial Intelligence (AI)-based auto-delineation technologies rapidly delineate multiple structures of interest like organs-at-risk and tumors in 3D medical images, reducing personnel load and facilitating time-critical therapies. Despite its accuracy, the AI may produce flawed delineations, requiring clinician attention. Quality assessment (QA) of these delineations is laborious and demanding. Delineation error detection systems aim to aid QA, yet questions linger about clinician adoption, challenges, and time-saving potential. In this study, we address these queries in two stages. First, we investigate the error detection workflow of a radiotherapy technologist and a radiation oncologist from Holland Proton Therapy Center, a Dutch cancer treatment center. The user study revealed which information sources clinicians prefer to use for the error prioritization task and elucidated clinicians' slice-based navigation workflows with and without system assistance. Based on the findings from the user study, we developed a simulation model of the QA process, which we used to assess different error detection workflows on a retrospective cohort of 42 head and neck cancer patients. The simulation study results indicate potential time savings through error and dose information, contingent on per-slice analysis time remaining near the current baseline. Our findings encourage the development of user-centric delineation error detection systems and provide a new way to model and evaluate these systems' potential clinical value.@en

Delineation of tumors and organs-at-risk permits detecting and correcting changes in the patients' anatomy throughout the treatment, making it a core step of adaptive proton therapy (APT). Although AI-based auto-contouring technologies have sped up this process, the time needed to perform the quality assessment (QA) of the generated contours remains a bottleneck, taking clinicians between several minutes up to an hour to complete. This paper introduces a fast contouring workflow suitable for time-critical APT, enabling detection of anatomical changes in shorter time frames and with a lower demand of clinical resources. The proposed AI-infused workflow follows two principles uncovered after reviewing the APT literature and conducting several interviews and an observational study in two radiotherapy centers in the Netherlands. First, enable targeted inspection of the generated contours by leveraging AI uncertainty and clinically-relevant features such as the proximity of the organs-at-risk to the tumor. Second, minimize the number of interactions needed to edit faulty delineations with redundancy-aware editing tools that provide the user a sense of predictability and control. We use a proof of concept that we validated with clinicians to demonstrate how current and upcoming AI capabilities support the workflow and how it would fit into clinical practice.@en

Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted contours. Bayesian models and their associated uncertainty, can potentially automate the process of detecting inaccurate predictions. We investigate two Bayesian models for auto-contouring, DropOut and FlipOut, using a quantitative measure – expected calibration error (ECE) and a qualitative measure – region-based accuracy-vs-uncertainty (R-AvU) graphs. It is well understood that a model should have low ECE to be considered trustworthy. However, in a QA context, a model should also have high uncertainty in inaccurate regions and low uncertainty in accurate regions. Such behaviour could direct visual attention of expert users to potentially inaccurate regions, leading to a speed-up in the QA process. Using R-AvU graphs, we qualitatively compare the behaviour of different models in accurate and inaccurate regions. Experiments are conducted on the MICCAI2015 Head and Neck Segmentation Challenge and on the DeepMindTCIA CT dataset using three models: DropOut-DICE, Dropout-CE (Cross Entropy) and FlipOut-CE. Quantitative results show that DropOut-DICE has the highest ECE, while Dropout-CE and FlipOut-CE have the lowest ECE. To better understand the difference between DropOut-CE and FlipOut-CE, we use the R-AvU graph which shows that FlipOut-CE has better uncertainty coverage in inaccurate regions than DropOut-CE. Such a combination of quantitative and qualitative metrics explores a new approach that helps to select which model can be deployed as a QA tool in clinical settings.
@en

Delineation of tumours and organs-at-risk permits detecting and correcting changes in the patients' anatomy throughout the treatment, making it a core step of adaptive external beam radiotherapy. Although auto-contouring technologies have sped up this process, the time needed to perform the quality assessment of the generated contours remains a bottleneck, taking clinicians between several minutes and an hour to complete. The authors of this article conducted several interviews and an observational study at two treatment centres in the Netherlands to identify challenges and opportunities for speeding up the delineation process in adaptive therapies. The study revealed three contextual variables that influence contouring performance: usable additional information, applicable domain-specific knowledge, and available editing capabilities in contouring software. In practice, clinicians leverage these variables to accelerate contouring in two ways. First, they use domain-specific knowledge and relevant clinical features such as the proximity of the organs-at-risk to the tumour to enable targeted inspection of the delineation. Second, clinicians modulate editing precision depending on the effect they anticipate the edit will have on the patient outcome. By implementing these acceleration strategies in guidelines and contouring tools, developers and workflow builders could increase contouring efficiency and consistency without affecting the patient outcome.@en

Cake customization services allow clients to collaboratively personalize cakes with pastry chefs. However, remote (e.g., email) and in-person co-design sessions are prone to miscommunication, due to natural restrictions in visualizing cake size, decoration, and celebration context. This paper presents the design, implementation, and expert evaluation of a social VR application (CakeVR) that allows a client to remotely co-design cakes with a pastry chef, through real-time realistic 3D visualizations. Drawing on expert semi-structured interviews (4 clients, 5 pastry chefs), we distill and incorporate 8 design requirements into our CakeVR prototype. We evaluate CakeVR with 10 experts (6 clients, 4 pastry chefs) using cognitive walkthroughs, and fnd that it supports ideation and decision making through intuitive size manipulation, color/favor selection, decoration design, and custom celebration theme ftting. Our fndings provide recommendations for enabling co-design in social VR and highlight CakeVR's potential to transform product design communication through remote interactive and immersive co-design.

@en

Background: Web-based patient education is increasingly offered to improve patients’ ability to learn, remember, and apply health information. Efficient organization, display, and structural design, that is, information architecture (IA), can support patients’ ability to independently use web-based patient education. However, the role of IA in the context of web-based patient education has not been examined systematically.

Objective: To support intervention designers in making informed choices that enhance patients’ learning, this paper describes a randomized experiment on the effects of IA on the effectiveness, use, and user experience of a patient education website and examines the theoretical mechanisms that explain these effects.

Methods: Middle-aged and older adults with self-reported hip or knee joint complaints were recruited to use and evaluate 1 of 3 patient education websites containing information on total joint replacement surgery. Each website contained the same textual content based on an existing leaflet but differed in the employed IA design (tunnel, hierarchical, or matrix design). Participants rated the websites on satisfaction, engagement, control, relevance, trust, and novelty and completed an objective knowledge test. Analyses of variance and structural equation modeling were used to examine the effects of IA and construct a theoretical model.

Results: We included 215 participants in our analysis. IA did not affect knowledge gain (P=.36) or overall satisfaction (P=.07) directly. However, tunnel (mean 3.22, SD 0.67) and matrix (mean 3.17, SD 0.69) architectures were found to provide more emotional support compared with hierarchical architectures (mean 2.86, SD 0.60; P=.002). Furthermore, increased perceptions of personal relevance in the tunnel IA (β=.18) were found to improve satisfaction (β=.17) indirectly. Increased perceptions of active control in the matrix IA (β=.11) also improved satisfaction (β=.27) indirectly. The final model of the IA effects explained 74.3% of the variance in satisfaction and 6.8% of the variance in knowledge and achieved excellent fit (χ217,215=14.7; P=.62; root mean square error of approximation=0.000; 95% CI [0.000-0.053]; comparative fit index=1.00; standardized root mean square residual=0.044).

Conclusions: IA has small but notable effects on users’ experiences with web-based health education interventions. Web-based patient education designers can employ tunnel IA designs to guide users through sequentially ordered content or matrix IA to offer users more control over navigation. Both improve user satisfaction by increasing user perceptions of relevance (tunnel) and active control (matrix). Although additional research is needed, hierarchical IA designs are currently not recommended, as hierarchical content is perceived as less supportive, engaging, and relevant, which may diminish the use and, in turn, the effect of the educational intervention.@en

Social Virtual Reality (VR) invites multiple users to "interact" in a shared immersive environment, which creates new opportunities for remote communication, and can potentially be a new tool for remote medical consultations. Using knee osteoarthritis consultation as a use case, this paper presents a social VR clinic that allows patients to consult a nurse represented as a virtual avatar with head, upper body and hands visible. We started with an ethnographic study at a hospital with three medical professionals and observed three patient consultation sessions to map the patient treatment journey (PTJ) and distill design requirements for social VR consultation. Based on the results of the study, we designed and implemented a social VR clinic to meet the identified requirements. Our work expands on the potential of social VR to help reshape patient treatment by reducing the workload of medical staff and the travel time of patients. In the future, we plan to conduct user studies to compare face-to-face (F2F) with social VR consultations.

@en

The authors explore the influence of the structure of a texture image on the perception of its color composition through a series of psychophysical studies. They estimate the color composition of a texture by extracting its dominant colors and the associated percentages. They then synthesize new textures with the same color composition but different geometric structural patterns. They conduct empirical studies in the form of two-alternative forced choice tests to determine the influence of two structural factors, pattern scale and shape, on the perceived amount of target color. The results of their studies indicate that (a) participants are able to consistently assess differences in color composition for textures of similar shape and scale, and (b) the perception of color composition is nonveridical. Pattern scale and shape have a strong influence on perceived color composition: the larger the scale, the higher the perceived amount of the target color, and the more elongated the shape, the lower the perceived amount of the target color. The authors also present a simple model that is consistent with the results of their empirical studies by accounting for the reduced visibility of the pixels near the color boundaries. In addition to a better understanding of human perception of color composition, their findings will contribute to the development of color texture similarity metrics.

@en

Visual material perception is often studied with physically well-defined stimuli that lack ecological variety. Yet, even the visual variety found in our natural environment is limited when compared to artistic depiction. A similar object can be depicted in numerous different ways that all make visual sense. We studied the perception of translucency using 38 paintings of sea waves as experimental stimuli. It has previously been shown that translucency depends on the shape of the translucent object and on the light conditions. Both shape and light appear in many variations in depictions of seas. In the first experiment we explored the use of Thurstonian scaling and introduce the concept of Number of Distinguishable Levels (NDL). We found that the NDL ranged between 1.5 in a set with small waves to 4 in a set with large waves. While Experiment 1 took place in the lab, Experiment 2 was performed online and replicated the data from Experiment 1 qualitatively, although the NDL was lower in the online experiment. Furthermore, in this experiment we conducted Thurstonian scaling on a number of other attributes that possibly contribute to translucency perception, such as wavetip shading, surface reflections and realism. We found that many of these correlated significantly with translucency. In sum, this study advocates and demonstrates the use of uncontrolled stimuli, in our case paintings, to explore the wide variety of input the human visual system can process.

@en

We previously presented a systematic optics-based canonical approach to test material-lighting interactions in their full natural ecology, combining canonical material and lighting modes. Analyzing the power of the spherical harmonics components of the lighting allowed us to predict the lighting effects on material perception for generic natural illumination environments. To further understand how material properties can be brought out or communicated visually, in the current study, we tested whether and how light map orientation and shape affect these interactions in a rating experiment: For combinations of four materials, three shapes, and three light maps, we rotated the light maps in 15 different configurations. For the velvety objects, there were main and interaction effects of lighting and light map orientation. The velvety ratings decreased when the main light source was coming from the back of the objects. For the specular objects, there were main and interaction effects of lighting and shape. The specular ratings increased when the environment in the specular reflections was clearly visible in the stimuli. For the glittery objects, there were main and interaction effects of shape and light map orientation. The glittery ratings correlated with the coverage of the glitter reflections as the shape and light map orientation varied. For the matte objects, results were robust across all conditions. Last, we propose combining the canonical modes approach with so-called importance maps to analyze the appearance features of the proximal stimulus, the image, in contradistinction to the physical parameters as an approach for optimization of material communication.

@en

This article describes a user-centered design experiment investigating positive and negative effects of adding game rules to brainstorms. We studied effects on brainstorm output and user experience and behavior. A coin-based gamification was developed with rules intended to improve brainstorm output in relation to quality and quantity of ideas. However, the invasiveness of a gamification can be expected to affect users both positively and negatively. To find an optimum between positive and negative effects of gamification invasiveness, we tested 5 different rule-sets with varying quantity and quality of rules. The results demonstrated that game rules stimulating competitive game behavior improved the quantity and quality of brainstorm output. Yet the invasiveness of the gamification also hindered this positive effect, due to discussions about rules and mandatory game behavior. From these results we deduced 3 types of invasiveness evoked by the rules’ qualities: a) governing rules led to negative cognitive invasiveness, b) forcing rules caused positive as well as negative behavioral invasiveness, and c) adding coins may have led to positive affective invasiveness (i.e., a playful attitude). We conclude our study with recommendations on designing and researching gamification invasiveness in real-life contexts.

@en

In this paper, we introduce a toolbox for the perceptually based visualization of light in a volume, focusing on the visual effects of illumination. First, our visualizations extend the conventional methods from a two-dimensional representation on surfaces to the whole volume of a scene. Second, we extend the conventional methods from showing only light intensity to visualizing three light properties (mean illuminance, primary direction and diffuseness). To make our methods generally available and easily accessible, we provide a web-based tool, to which everybody can upload data, measured by a cubic or simple illuminance meter or even a smartphone-app, and generate a variety of three-dimensional visualizations of the light field. The importance of considering the light field in its full complexity (and thus as a three-dimensional vector field instead of its two-dimensional sections) is widely acknowledged. Our toolbox allows easy access to sophisticated methods for analysing the spatial distribution of light and its primary qualities as well as how they vary throughout space. It is our hope that our results raise interest in ‘third stage’ approaches to lighting research and design, and the toolbox offers a practical solution to this complex problem.

@en