Uncertainty guidance in proton therapy planning visualization

Musleh, Maath; Muren, L.P.; Toussaint, Laura; Vestergaard, A.; Gröller, Eduard; Raidou, Renata Georgia

doi:10.1016/j.cag.2023.02.002

Cited by 7 publications

(1 citation statement)

References 39 publications

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“…The inability of clinicians to effectively establish a frame when switching between noncontiguous slices could explain this behavior . A way to alleviate this difficulty would be by providing clinicians with more effective means to update their mental model or by using less intrusive guidance (Musleh et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Implementation of Delineation Error Detection Systems in Clinical Practice: Do AI-Supported Optimization and Human Preferences Meet?

Chaves-de-Plaza,

Mody,

Hildebrandt

et al. 2023

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Implementation of Delineation Error Detection Systems in Clinical Practice: Do AI-Supported Optimization and Human Preferences Meet?

Chaves-de-Plaza,

Mody,

Hildebrandt

et al. 2023

Preprint

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A framework for visual comparison of scalar fields with uncertainty

Leonhardt,

Wiebel,

Garth

2024

Vis Comput

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Scientists working with uncertain data, such as climate simulations, medical images, or ensembles of physical simulations, regularly confront the problem of comparing observations, e.g., to identify similarities, differences, or patterns. Current approaches in comparative visualization of uncertain scalar fields mainly rely on juxtaposition of both data and uncertainties, where each is represented using, e.g., color mapping or volume rendering. While interpretation of uncertain scalar data from visual encodings is already cognitively challenging, comparison of uncertain fields without explicit visualization support adds a further layer of complexity. In this paper, we present a theoretical framework to devise and describe a class of techniques that directly visualize differences between two or more uncertain scalar fields in a single image. We model each such technique as a combination of one or more interpolation stages, with the application of distance measures on random variables to the resulting distributions, and an appropriate visual encoding. Our framework captures existing methods and lends itself well to formulating new comparative visualization techniques for uncertain data for different visualization scenarios. Furthermore, by modeling uncertain scalar field differences as random variables themselves, we enable additional opportunities for comparison. We demonstrate the usefulness of our framework and its properties by applying it to effective comparative visualization techniques for several synthetic and real-world data sets.

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