MODELAR: A MODular and EvaLuative framework to improve surgical Augmented Reality visualization

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dc.contributor.authorHattab, Georgesen_US
dc.contributor.authorMeyer, Felixen_US
dc.contributor.authorAlbrecht, Remke Dirken_US
dc.contributor.authorSpeidel, Stefanieen_US
dc.contributor.editorKerren, Andreas and Garth, Christoph and Marai, G. Elisabetaen_US
dc.date.accessioned2020-05-24T13:52:12Z
dc.date.available2020-05-24T13:52:12Z
dc.date.issued2020
dc.description.abstractThe use of Augmented Reality (AR) for the visualization of 3D biomedical image data is possible thanks to a growing number of hardware and software solutions. Considerable efforts are made during surgery, where the visual information of the target structures can either be highlighted or dulled. However, as technical challenges and barriers to development decrease, it's increasingly important to take into account the specific capacities and constraints of the surgeon's perceptual and cognitive systems. To address this legitimate problem, we present a practical framework that evaluates the importance of visual encodings and renderings for surgical AR. By conducting a task-specific user study we observed a set of emerging visualization strategies. The given task is to make the kidney boundary visually salient and make the tumor and calyx distinguishable. After having recruited 23 participants, we found two preferred presets to tackle this task. With both presets, the usage of color, depth, and opacity improved the display of the organ bounds while contrasting the tumor and calyx. 19 participants successfully completed the task using MODELAR. Their preference was to either find a good preset where the organ bounds were visible then adjust the color of target objects or vice versa. MODELAR helped us better identify effective visualization that best fit the task requirements. Our evaluation results and the modular framework MODELAR is freely available and open source at https://github.com/ghattab/MODELAR.en_US
dc.description.sectionheadersRendering, Images, and Applications
dc.description.seriesinformationEuroVis 2020 - Short Papers
dc.identifier.doi10.2312/evs.20201066
dc.identifier.isbn978-3-03868-106-9
dc.identifier.pages163-167
dc.identifier.urihttps://doi.org/10.2312/evs.20201066
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20201066
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectH.5.1 [Computer Graphics]
dc.subjectMultimedia Information Systems
dc.subjectArtificial
dc.subjectaugmented
dc.subjectand virtual realities H.5.1 [Computer Graphics]
dc.subjectMultimedia Information Systems
dc.subjectEvaluation/methodology
dc.subjectI.3.7 [Computer Graphics]
dc.subjectThree Dimensional Graphics and Realism
dc.subjectColor
dc.subjectshading
dc.subjectshadowing
dc.subjectand texture
dc.titleMODELAR: A MODular and EvaLuative framework to improve surgical Augmented Reality visualizationen_US
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