The Medical Imaging Interaction Toolkit

Wolf, Ivo; Vetter, Marcus; Wegner, Ingmar; Böttger, T; Nolden, Marco; Schöbinger, Max; Hastenteufel, Mark; Kunert, Tobias; Meinzer, Hans Peter

doi:10.1016/j.media.2005.04.005

Cited by 370 publications

(226 citation statements)

References 7 publications

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“…Therefore, CT scans were prepared under normal breathing and mechanical ventilation at different times. The images were segmented from the Division of Medical and Biological Informatics of the University of Heidelberg by a regiongrowing technique and the surface of the bronchial tree was obtained by a 3D-skeleton method [60] from a system based on Medical Imaging Interaction Toolkit, see Wolf et al [61]. The surface model of the bronchial tree was stored in stereolithography format (.stl).…”

Section: Background Information For Results Presentationmentioning

confidence: 99%

Fluid–structure interaction in lower airways of CT‐based lung geometries

Wall

Rabczuk

2008

Numerical Methods in Fluids

105

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SUMMARYIn this study, the deformability of airway walls is taken into account to study airflow patterns and airway wall stresses in the first generations of lower airways in a real lung geometry. The lung geometry is based on CT scans that are obtained from in vivo experiments on humans. A partitioned fluid-structure interaction (FSI) approach, realized within a parallel in-house finite element code, is employed. It is designed for the robust and efficient simulation of the interaction of transient incompressible Newtonian flows and (geometrically) non-linear airway wall behavior. Arbitrary Lagrangian-Eulerian-based stabilized tetrahedral finite elements are used for the fluid and Lagrangian-based 7-parametric mixed/hybrid shell elements are used for the airway walls using unstructured meshes due to the complexity of the geometry. Airflow patterns as well as airway wall stresses in the bronchial tree are studied for a number of different scenarios. Thereby, both models for healthy and diseased lungs are taken into account and both normal breathing and mechanical ventilation scenarios are studied.

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Section: Background Information For Results Presentationmentioning

confidence: 99%

Fluid–structure interaction in lower airways of CT‐based lung geometries

Wall

Rabczuk

2008

Numerical Methods in Fluids

105

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“…In this sense, and in order to assess the accuracy of the competitive strategy in a different imaging modality, a manual contouring of the RA and the aortic tract was additionally performed. The RA was segmented using the MITK software (Medical Imaging Interaction Toolkit, (Wolf et al, 2005)), where multiple 2D slices were delineated and then interpolated to a 3D surface. Moreover, bounding boxes and valve plane were generated as explained in section 3.1.…”

Section: Ground Truth Generationmentioning

confidence: 99%

A competitive strategy for atrial and aortic tract segmentation based on deformable models

Morais

Vilaça

Queirós

et al. 2017

Medical Image Analysis

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A competitive strategy for atrial and aortic tract segmentation based on deformable models AbstractMultiple strategies have previously been described for atrial region (i.e. atrial bodies and aortic tract) segmentation. Although these techniques have proven their accuracy, inadequate results in the mid atrial walls are common, restricting their application for specific cardiac interventions. In this work, we introduce a novel competitive strategy to perform atrial region segmentation with correct delineation of the thin mid walls, and integrated it into the B-spline Explicit Active Surfaces framework. A double-stage segmentation process is used, which starts with a fast contour growing followed by a refinement stage with local descriptors. Independent functions are used to define each region, being afterward combined to compete for the optimal boundary. The competition locally constrains the surface evolution, prevents overlaps and allows refinement to the walls. Three different scenarios were used to demonstrate the advantages of the proposed approach, through the evaluation of its segmentation accuracy, and its performance for heterogeneous mid walls. Both computed tomography and magnetic resonance imaging datasets were used, presenting results similar to the state-of-the-art methods for both atria and aorta. The competitive strategy showed its superior performance with statistically significant differences against the traditional freeevolution approach in cases with bad image quality or missed atrial/aortic walls. Moreover, only the competitive approach was able to accurately segment the atrial/aortic wall. Overall, the proposed strategy showed to be suitable for atrial region segmentation with a correct segmentation of the mid thin walls, demonstrating its added value with respect to the traditional techniques.

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“…However, VTK still lacks multivolume rendering as reported by the visualization literature and extensions for multivolume visualization, for instance,12 have not found their way into the framework yet. Research platforms, such as 3D Slicer13 and the Medical Imaging Interaction Toolkit (MITK)14 which are tailored to medical applications often use VTK as basis for the visualization. They offer solutions to more specific clinical applications or workflows, but they also target data processing aspects and try not necessarily to improve the visualization.…”

Section: Introductionmentioning

confidence: 99%

Visualization of 4D multimodal imaging data and its applications in radiotherapy planning

Schlachter

Fechter

Adebahr

et al. 2017

J Applied Clin Med Phys

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PurposeTo explore the benefit of using 4D multimodal visualization and interaction techniques for defined radiotherapy planning tasks over a treatment planning system used in clinical routine (C‐TPS) without dedicated 4D visualization.MethodsWe developed a 4D visualization system (4D‐VS) with dedicated rendering and fusion of 4D multimodal imaging data based on a list of requirements developed in collaboration with radiation oncologists. We conducted a user evaluation in which the benefits of our approach were evaluated in comparison to C‐TPS for three specific tasks: assessment of internal target volume (ITV) delineation, classification of tumor location in peripheral or central, and assessment of dose distribution. For all three tasks, we presented test cases for which we measured correctness, certainty, consistency followed by an additional survey regarding specific visualization features.ResultsLower quality of the test ITVs (ground truth quality was available) was more likely to be detected using 4D‐VS. ITV ratings were more consistent in 4D‐VS and the classification of tumor location had a higher accuracy. Overall evaluation of the survey indicates 4D‐VS provides better spatial comprehensibility and simplifies the tasks which were performed during testing.ConclusionsThe use of 4D‐VS has improved the assessment of ITV delineations and classification of tumor location. The visualization features of 4D‐VS have been identified as helpful for the assessment of dose distribution during user testing.

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The Medical Imaging Interaction Toolkit

Cited by 370 publications

References 7 publications

Fluid–structure interaction in lower airways of CT‐based lung geometries

Fluid–structure interaction in lower airways of CT‐based lung geometries

A competitive strategy for atrial and aortic tract segmentation based on deformable models

Visualization of 4D multimodal imaging data and its applications in radiotherapy planning

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