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

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摘要 Keyhole surgeries become mcreasingly important in clinical daily routine as they help minimizing the damage of a patient's healthy tissue.The planning of keyhole surgeries is based on medical imaging and an important factor that influences the surgeries*success.Due to the image reconstruction process,medical image data contains uncertainty that exacerbates the planning of a keyhole surgery.In this paper we present a visual workflow that helps clinicians to examine and compare different surgery paths as well as visualizing the patientsJ affected tissue.The analysis is based on the concept of hierarchical image semantics,that segment the underlying image data with respect to the input images'uncertainty and the users understanding of tissue composition.Users can define arbitrary surgery paths that they need to investigate further.The defined paths can be queried by a rating function to identify paths that fulfill user-defined properties.The workflow allows a visual inspection of the affected tissues and its substructures.Therefore,the workflow includes a linked view system indicating the three-dimensional location of selected surgery paths as well as how these paths affect the patients tissue.To show the effectiveness of the presented approach,we applied it to the planning of a keyhole surgery of a brain tumor removal and a kneecap surgery.
出处 《Visual Informatics》 EI 2018年第1期26-36,共11页 可视信息学(英文)
基金 This research was funded by the German Research Foundation(DFG)within the IRTG 2057"Physical Modeling for Virtual Manufacturing Systems and Processes”.
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