An Uncertainty-aware Workflow for Keyhole Surgery Planning using Hierarchical Image Semantics

Gillmann, Christina; Maack, Robin G. C.; Post, Tobias; Wischgoll, Thomas; Hagen, Hans

doi:10.1016/j.visinf.2018.04.004

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…Many techniques attempt to create a readable layout that follows certain rules. A straightening lays out the branches of a tree as straight lines, which can be connected [EMKL21] or disconnected [GWH15]. The advantage of straightening is that the individual segments can be easily visually followed.…”

Section: Layout Generationmentioning

confidence: 99%

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Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

Eulzer

Meuschke²,

Mistelbauer

et al. 2022

Computer Graphics Forum

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Input Graph LayoutFigure 1: We describe the creation of a vessel map as a three-step process: the layout of the map must be derived, the geometry or spatiality of the data must be mapped, and the data attributes must be mapped. We differentiate between image volume maps, flow volume maps, vessel wall maps, and vessel network maps depending on the primary data structure the map-like visualization is based on.

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Section: Layout Generationmentioning

confidence: 99%

“…Gillmann et al [GWH15] present a visualization for coronary artery analysis, especially for surgical preparation, intervention, and restoration. Coronary artery centerlines are obtained from coronary CT data sets.…”

Section: Roos Et Al [Rfk * 07] Evaluated Multipath Cpr In a Clinicalmentioning

confidence: 99%

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

Eulzer

Meuschke²,

Mistelbauer

et al. 2022

Computer Graphics Forum

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show abstract

“…Gillmann et al [GMP∗18] provided an uncertainty‐aware tool that allows planning minimally invasive surgeries. In their tool, the uncertainty of the input CT scans is captured while an uncertainty‐aware segmentation approach is used to extract different types of tissues.…”

Section: Applicationsmentioning

confidence: 99%

Uncertainty‐aware Visualization in Medical Imaging ‐ A Survey

Gillmann

Saur

Wischgoll

et al. 2021

Computer Graphics Forum

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Medical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision‐making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state‐of‐the‐art in uncertainty‐aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be combined to form uncertainty‐aware medical imaging pipelines. Based on our analysis, we are able to point to open problems in uncertainty‐aware medical imaging.

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“…Many medical applications are affected by uncertainty mainly originating from missing data, ambiguous data, or data that is affected by error sources in the data acquisition process. Examples are lesion examination [28], surgery assistance [29] and vascular analysis [73]. This leads to a decision-making process that is highly affected by uncertainty.…”

Section: Medicinementioning

confidence: 99%

Uncertainty-aware Visual Analytics - Scope, Oppertunities and Challenges

Maack

Scheuermann

Hagen

et al. 2021

Preprint

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In many applications, Visual Analytics(VA) has developed into a standard tool to ease data access and knowledge generation. Unfortunately, many data sources, used in the VA process, are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process. The classic VA cycle does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization. Different data types and application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of uncertainty-aware VA are provided.

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An Uncertainty-aware Workflow for Keyhole Surgery Planning using Hierarchical Image Semantics

Cited by 9 publications

References 12 publications

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

Uncertainty‐aware Visualization in Medical Imaging ‐ A Survey

Uncertainty-aware Visual Analytics - Scope, Oppertunities and Challenges

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